DEPARTMENT OF ECE
IV YEAR / VII SEMESTER
EC 8071 – COGNITIVE RADIO
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EC 8071- COGNITIVE RADIO
UNIT I: INTRODUCTION TO SOFTWAREDEFINED RADIO AND COGNITIVE RADIO
Evolution of Software Defined Radio and
Cognitive radio: goals, benefits, definitions,
architectures, relations with other radios, issues,
enabling technologies, radio frequency spectrum
and regulations.
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INTRODUCTION
• Data communication plays a vital role in society.
• With the exponential growth in the ways and means by which
people need to communicate –
➢ Data communications
➢ voice communications
➢ video communications
➢ broadcast messaging
➢ command and control Communications
➢ Emergency response communications
• Modifying radio devices easily and cost-effectively has become
business.
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SOFTWARE DEFINED RADIO
BASIC CONCEPT
• Radio can be totally configured or defined by
the software so that a common platform can be
used across a number of areas.
• There is also the possibility that it can then be
re-configured as upgrades to standards.
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RADIO WAVES
• The range of the radio spectrum is considered to
be 3 kilohertz(KHz) up to 300 gigahertz(GHz).
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RADIO WAVES
▪ Like waves on a pond, a radio wave is a series of repeating
peaks and valleys.
▪ The entire pattern of a wave.
▪ A radio wave is generated by a
transmitter and then detected by
a receiver.
▪ An antenna allows a radio transmitter to send energy into
space and a receiver to pick up energy from space.
▪ Transmitters and receivers are typically designed to operate
over a limited range of frequencies.
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SOFTWARE DEFINED
RADIO(SDR)
DEFINITION:
• Radio in which some or all of the Physical Layer Functions
are software-defined
• Software-defined refers to the use of software processing
within the radio system or device to implement operating (but
not control) functions
• Software for this definition refers to modifiable instructions
executed by a programmable processing device
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SOFTWARE DEFINED RADIO
• SDR refers to technologies wherein the functionalities are
performed by software modules.
• The characteristics of radio such as coding, modulation type,
frequency band can be changed at will simply by loading a
new software.
• Multiple radio devices using different modulations can be
replaced by a single radio device.
• SDR, currently used to build radios that support multiple
interface technologies(CDMA, GSM, WiFi) with a single
modem by reconfiguring it in software.
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SDR-EVOLUTION
IN THE UNITED STATES
• SDR concept started in the late 1970s with the introduction of
multimode radios operating in VHF band
• U.S. Air Force Avionics Laboratory initiated the Integrated
Communication, Navigation, Identification and Avionics
(ICNIA) program in the late 1970s
• Developed an architecture to support multifunctional,
multiband airborne radios in the 30 MHz -1600 MHz band
• Successful flight test and final report delivery in 1992
• ICNIA radio was the first programmable radio
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SDR-EVOLUTION
IN THE UNITED STATES
• In the late 1980s, the Air Force Research Laboratory initiated
the Tactical Anti-Jam Programmable Signal processor (TAJPSP)
• Developed a processor capable of simultaneous waveform
operations using modular approach – TAJPSP later evolved into
the SPEAK easy program
• SPEAK easy was a joint U.S. Government program to develop
the architecture and technology to meet future military
requirements for multimedia networking operations
• The first significant military investment to integrate various
existing radio families into one family
• SPEAK easy evolved into the Joint Tactical Radio System
(JTRS)
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SDR-EVOLUTION
IN THE UNITED STATES
• JTRS Joint Program Office was established in 1999
• Envisioned to be the next generation tactical radio for future
advanced military operations
• Mission is to “acquire a family of affordable, high-capacity
tactical radios to
• provide interoperable LOS/BLOS C4I capabilities to the war
fighters”
• The SDR Forum provides expertise in software radio
technology for the JTRS program
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SDR-EVOLUTION IN EUROPE
• R&D in Advanced Communications in Europe (RACE)
and Advanced Communications Technology and Services
(ACTS) programs.
• ACTS projects, FIRST and FRAMES, used software radios
to investigate next-generation air-interfaces
• FIRST: Flexible Integrated Radio System and Technology
• FRAMES: Future Radio Wideband Multiple Access System
• RACE and ACTS focus on incorporating 3G and potentially
4G standards into its Global System for Mobile (GSM)
Communications network
– Pave the way for more capable and more powerful products and
flexible services
• – Key research areas include receiver architecture, baseband DSP
architecture, enabling technologies
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SDR-EVOLUTION IN ASIA
• In 1999, Japanese Institute of Electronics, Information
and Communication Engineers (IEICE) software radio
group was formed
• Held technical conferences, workshops, panel
discussions and symposia, in conjunction with SDR
Forum Radio.
• In 2000, Korea Electromagnetic Engineering Society
(KEES) sponsored a workshop to monitor software radio
activities in Korea, Japan and Taiwan
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SDR-EVOLUTION IN ASIA
• IEICE and KEES mission:
– Promote R&D in SDR
– Allow protocol, software, hardware to be easily
integrated for future radio system
– Foster cross-organization and collaboration among
academia, industries and governments
– Organize symposia and workshops on SDR
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COGNITIVE RADIO
COGNITION:
• The mental action or process of acquiring knowledge
and understanding through thought, experience, and the
senses
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COGNITIVE RADIO
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COGNITIVE RADIO
• A cognitive radio may be defined as a radio that is aware of
its environment and the internal state and with knowledge of
these elements and any stored predefined objectives can make
and implement decisions about its behavior.
• Utilizes Software Defined Radio, Adaptive Radio, and other
technologies to automatically adjust its behavior or operations
to achieve desired objectives.
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COGNITIVE RADIO
• Hykin defined cognitive radio as a radio capable of being
aware of its surroundings, learning and adaptively changing its
operating parameters in real time with the objective of
providing reliable anytime, anywhere and spectrally efficient
communication.
• U.S Federal Communications commission(FCC) defines that
cognitive radio that can change its transmitter parameters
based on interaction with the environment in which it operates.
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COGNITIVE RADIO
Two Main Characteristics:
• Reconfigurability
• Intelligent adaptive behavior
Cognitive radio functionality requires following capabilities:
• Flexibility and agility- Ability to change the waveform and
other radio operational parameters.
- Reconfigurable
- Agility- ability to think quickly
• Sensing – Observe and measure the state of the environment
including spectral occupancy.
• Learning and adaptability - Analyse sensory input,
recognize patterns, modify internal operational behaviour
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EVOLUTION OF COGNITIVE RADIO
• Main research work was carried out by Mitola and Maguire in
1999 and early spectrum measurement studies conducted in
1995 to quantify the use of spectrum.
IN UNITED STATES:
• Focused on dynamic spectrum access(DSA) and secondary
use of spectrum
• The main goal of spectrum management and policy research
project is to study policy servers and secondary use
technologies particularly for military purposes.
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EVOLUTION OF COGNITIVE RADIO
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EVOLUTION OF COGNITIVE RADIO
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EVOLUTION OF COGNITIVE RADIO
IN UK
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IN UK
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Relationship between SDR and CR
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Relationship between SDR and CR
• Main characteristic of CR is adaptability of radio parmeters
(frequency, power, modulation bandwidth) and can be changed
depending on radio environment, user situation , network
condition, geo location.
• SDR provide flexible radio functionality by avoiding the use
of application specific fixed analog circuits and components.
• SDR – core enabling technology for CR
• CR is wrapped around SDR
• Combination of cognitive engine, SDR and other supporting
functionalities results in CR.
• Cognitive engine is responsible for optimizing and controlling
the SDR and aware of radio hardware resources and
capabilities.
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Relationship between SDR and CR
• SDR is built around software based digital signal processing
along with software tunable radio frequency components.
• SDR is capable of operating with different bandwidth over
wide range of frequencies.
• SDR supports multiple standards(GSM,EDGE, CDMA2000,
Wi-Fi, WiMAX) and multiple access technologies(TDMA,
CDMA, OFDM, SDMA)
• Sensing devices are required to sense the spectrum, which can
be either embedded into SDR internally or incorporated to
SDR externally.
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Relationship between SDR and CR
• For Example- Antenna can considered as internal sensor and
video camera can be considered as external sensor.
• SDR provides spectrum information to cognitive engine.
• The captured spectrum is digitized by Analog to digital
converter and then the samples are sent to digital signal
processor for post processing.
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Benefits of SDR
• Accommodate multiple standards.
• Allow multiple services and incentives
• Capable for insertions of future technologies and allow easy
upgrades.
• Implement open architecture to allow multiple vendors to
supply, offer declining prices and reduce product development
time.
• Enable other advanced commercial technologies to offer user
services and benefits.
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Benefits of SDR
•
•
•
•
•
•
SDR offers the greatest flexibility
Develop software to perform signal processing
Developing and debugging software is much easier.
Offers service upgrades and bug fixes.
This capability saves time and cost of design and deployment
Reusability of software
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Benefits of SDR
• Written with the concept of modular code, software can be
ported between processors with minimal rewriting required.
• Instead of FPGA – based SDR systems where very high speed
integrated circuit hardware description language (VHDL) is
used GPP(General purpose processor)- based SDR system is
used where code portability is advantageous.
• Easy to test individual signal processing blocks, simulate
performance and test behavior in a closed system and then
reuse the same software for a real, over the air system.
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Benefits of CR
IMPROVED COMMUNICATION STRUCTURE:
Communication with other networks:
• Exists multiple public safety standards.
• For eg. When a large disaster occurs at country borders, if
countries use different technologies, it will be a challenge
• Cognitive radio will support for military standards and other
public safety standards would solve this problem.
Backwards compatibility:
• Large investments and small market are replaced slowly and
coexist with new communication networks for a long time.
• CR allows an upgrade of the existing equipment
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Benefits of CR
Introduction of new services:
• New services could be enabled more easily as it can adjust its
parameters according to the requirements of the new service.
Improved reliability:
• CR tries to minimize interference to other networks by
changing its frequency if other signals are present.
Enabling Broadband:
• CR is used to sense empty frequency bands(white space) and
use it as secondary user to set up an auxiliary communication
network.
• Large bandwidth requirement could be provided by secondary
spectrum usage.
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Architecture of SDR
• Architecture is the framework of different types of
components utilized to attain specific functions with certain
limits and rules.
• SDR is a collection of hardware and software technologies
where operating functions are implemented through
modifiable software or firmware operating on programmable
processing technologies.
• These devices include FPGA, DSP, GPP, programmable
System on Chip(SoC)
• These technologies allows new wireless features and
capabilities without requiring new hardware.
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Architecture of SDR
• Functions of the software Radio Technology:
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Architecture of SDR
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Architecture of SDR
• Evolution support is necessary to define waveform
personalities and to assure that each new personality is safe.
• Multiband technology accesses more than one RF band of
communications channel at once.
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Architecture of SDR
Functional model of software radio
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Architecture of SDR
Channel Set: Have RF channels
• Radio nodes like PCS-personal communications system base
stations and portable military radios interconnect to fiber and
cable.
Channel encoder: RF/channel access, IF processing and modem
• Antennas and RF conversion span multiple RF bands comprise
the RF/Channel access function
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Architecture of SDR
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Architecture of SDR
Joint control function
• Implemented using multithreaded multiprocessor software
ensures system stability, error recovery, timely data flow, and
isochronous streaming of voice and video.
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Architecture of SDR
Analog Stream
• Used to interface the external audio, video and facsimile
devices with the SDR.
• These interfaces may carry continuous streams
Source Bit Stream
• This interface carries the coded bit streams and packets.
• It also carries the signals from ADC, Vocoders, and
compressed Text.
• Sampling theorem is used for ADC and finite arithmetic
precision for coded sequences.
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Architecture of SDR
Clear Bit Streams
• These streams are framed, multiplexed and FEC performed
packets
Protected Bit stream
• They carry the authentication responses, public key and private
key.
• It also carries the enciphered bit streams.
• IF Wave form
• It carries digitally pre emphasized waveform ready for up
conversion
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Architecture of SDR
RF Waveform
• This interface deals with the control of power level, adjacent
channel interference etc.,
Network interface
• These interface are used to carry the information form the
remotely located sources.
• This interface uses asynchronous Transfer Mode (ATM) and
its associated protocols.
• ATM is a telecommunication concept to transfer the user
traffic including Voice, data and video signals.
• They are widely used for the integrated services digital
networks
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Architecture of SDR
Joint Control
• This controls all the interfaces between the hardware and
software.
• This interface is also used for initialization and fault recovery
Standardizing an Interface
• This way, vendors can develop their waveforms independent
of the knowledge of the underlying hardware.
• Similarly, hardware developers can develop a radio with
standardized interfaces, which can subsequently be expected to
run a wide variety of waveforms from standardized libraries.
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Functional components of Software
Radio
• Software radio defines all feature of the air interface including
RF channel access and waveform fusion in software.
• ADC and DAC transform is possible and each RF service
among digital and analog forms at IF.
• Resulting digitized stream of bandwidth,Ws, accommodates
all subscriber channels with bandwidth Wc
• Where Ws >> Wc
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Fig. Key Software Radio Functions and
Components
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Functional components of Software
Radio
• IF processing may include filtering to isolate subscriber
channels.
• Acquire high quality waveform
• There are multiple IF frequencies
• For direct conversion receiver- IF processing is null(minimize
interference)
• Digital down conversion is the process of converting
frequency domain samples to baseband waveform
• Pre-selection filters with the required performance are reduced
in size to get better performance.
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Functional components of Software
Radio
• In a software radio transmitter, baseband signals are
transformed into sampled channel waveform via modems
functions
• Implemented in software using high performance DAC and
dynamically reconfigurable FPGA.
• Output signals are pre-emphasized or non-linearly pre-coded
by IF processor
• In some implementation, modem functions, IF processing and
RF channel access is integrated into a single component as in
direct conversion receiver
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ARCHITECTURE OF CR
• Architecture is a comprehensive, consistent set of design rules by which a
specified set of components achieves a specified set of functions in
products and services that evolve through multiple design points over time
• Some additional functions are added to the SDR so that the architecture can
sense the information autonomously and tailor the information according
the needs of the user.
• Cognitive – a radio must be self aware
• Using that knowledge, it should know a minimum set of basic facts about
radio and it should be able to communicate with other entities.
• For example, it should know that an equalizer time domain taps reflect the
channel impulse response.
• Cognitive radio contain a computational model of itself including the
equalizer’s structure and function.
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Ideal Architecture of cognitive radio
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Cognitive Radio Components-Modules
• Radio that uses RKRL(Radio knowledge Representation
Language) could be organized as shown.
• System hardware consists of a set of modules: Antenna, RF
section, modem, INFOSEC module, baseband/protocol
processor and user interface.
• The baseband processor hosts the protocol and control
software.
• Modem software includes the modem with equalizer.
• The architecture are defined in UML(Unified Modeling
Language) object models, CORBA(Common Object Request
Broker Architecture) Interface design language.
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ARCHITECTURE OF CR
• A cognitive radio contains an internal model of its own
hardware and software structure.
• Variable bindings between the equalizer model and the
software equalizer establish the interface between the
reasoning capability and the operational software.
• Cognition defines SDR in RXML(Radio eXtensible Markup
Language) which includes RF knowledge, structured
reasoning and Ad-Hoc reasoning.
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ARCHITECTURE OF CR
• The model-based reasoning capability that applies these RKRL
frames to solve radio control problems gives the radio its
cognitive ability.
• This approach is used to represent radio knowledge in RKRL
and to construct reasoning algorithms to use that knowledge
for the control of software radios.
• CR’s model should contain a representation of its functions
such as transmission, reception, coding and mechanisms
involved in antenna, RF conversion, DSP.
• CORBA IDL initiates a starting point.
• CR is an SDR with flexible, formal and semantic based entity
to entity messaging through RXML and integrated machine
learning.
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ARCHITECTURE OF CR
The Three computational Intelligence aspects of CR are as
follows:
• Radio Knowledge
• User Knowledge
• The Capacity to Learn
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Radio Knowledge
• Radio knowledge has to be translated into a body of
computationally accessible, structured technical knowledge
about radio.
RXML : RF– Radio Extensible Markup Language
• RXML is the primary enabler and product which helps the
formalization of radio knowledge.
• RXML will enable the structuring of sufficient RF and user
world knowledge to build advanced wireless-enabled or
enhanced information services.
• These information should meet the levels of accuracy defined
by the international bodies like ITU(International
Telecommunication Union).
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User Knowledge
• User knowledge is formalized at the level of abstraction and
degree of detail necessary to give CR the ability to acquire,
from its owner and other designated users relevant to
information services.
• This gives introduction to machine learning.
• Machine recognizes the opportunities for learning.
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ARCHITECTURE OF CR
• CRA1(defines functions, components and design rules) is a
rapid prototype which tightly integrates RKRL(Radio
knowledge Representation Language) frames into the modelbased reasoning architecture.
• CR’s model of its internal structure should then sustain
systems-level interactions with the network.
• If the radio is to be context aware, it must interact with the
outside world.
• This is done by cognition cycle.
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Relations with other radios
List of radio technology proposed by GSC(Global Standards
Collaboration) group
• Two new radio classes
• Specific implementations of cognitive radio
Two Radio classes:
• Policy based Radios - Emphasis on how cognitive radio
technology can impact the development and implementation of
communications policy.
• DFS(Dynamic Frequency Selection) Radios - advanced radio
technologies are enabling a multitude of new radio concepts.
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Relations with other radios
Policy based Radios :
• Governed by a predetermined set of rules for behavior
• Rules define operating limits
Rules can be defined and implemented:
• During manufacture
• During configuration of a device by the user
• During over the air provisioning
• Over the air control
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Software reconfigurable radio
• Incorporates software controlled antenna filters to dynamically
select receivable frequencies
• Capable of downloading and installing updated software for
controlling operational characteristics and antenna filters
without manual intervention.
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DFS(Dynamic Frequency Selection)
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DFS(Dynamic Frequency Selection)
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DFS(Dynamic Frequency Selection)
• DFS is used for spectrum management of 5GHz channels by
OFDM radio devices.
• The European Radiocommunications committee(ERC) and
FCC mandate that radio cards operating in the 5 GHz band
implement mechanism to avoid interference with radar system.
• DFS is essentially radar detection and radar interference
avoidance technology.
• DFS service is used to meet these regulatory requirements.
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Issues
• Public safety radio lies in the ability of software defined radios
to reconfigure the operating characteristics rapidly simply by
changing the inherent software and instantiating a different
software package to provide the desired operating
requirements in terms of over the air characteristics as well as
information/networking requirements such as coding.
• The ability to do Over-the-Air-Programming (OTAP) becomes
an extremely valuable capability for software defined radios in
a public safety scenario.
• The National Institute of Justice (NIJ) recognized the potential
of SDRs and suggested several Key Points for Public Safety
Communications.
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Issues
• The first key issue is simply the ability to convert from analog
to digital (A-D) signals.
• From the host user’s perspective, this is common practice now
over any number of devices.
• If A-D conversion can take place immediately at the antenna,
then all functions within the radio can be done digitally (i.e. in
software).
• Using Sampling Theorem the signals must be sampled at a rate
at least twice the highest frequency in order to be able to
accurately and faithfully reproduce the original signal from its
sampled values.
• Each sample value must be converted to a digital
representation
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Issues
• The second issue is the timing and synchronization required
within the radio.
• Maintains the integrity of information throughout the radio.
• It is also critical in establishing the timing relationships
between the general purpose processors that are handling part
of the software functions and operating at an independent
clock speed.
• A third major design consideration is the interface to the RF
transmission domain.
• Antennas propagate signals differently based on the frequency
of the signal being transmitted and optimizing RF signal
propagation is therefore frequency dependent.
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Issues
• As it is difficult to design antennas over a wide range of
frequencies.
• The electronic circuits leading to the antennas must be
matched to the antenna’s electrical characteristics for
maximum power transfer.
• The result of this “real world” problem is that multiple
antennas – matched to particular frequency bands – must be
used.
• This complicates the radio design as well as the antenna design
– particularly if multiple input/multiple output antennas are
used for part of the transmission process.
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SDR Issues
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SDR Enabling Technologies
NCO- Numerically controlled oscillator; RSP- Receive signal processor
TSP- Transmit signal processor
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SDR Enabling Technologies
Antennas:
• Receive antennas are easier to achieve wide band
performance than transmit ones
Waveforms:
• Management and selection of multiple waveforms
• Cancellation carriers and pulse shaping are relatively
new techniques.
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SDR Enabling Technologies
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SDR Enabling Technologies
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SDR Enabling Technologies
• Design tools
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SDR Advantages
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SDR Advantages
• SDR provide software control of a variety of modulation
techniques, wideband and narrowband operation, transmission
security functions and waveform requirements.
• Single system can operate under multiple configurations,
providing interoperability, bridging and tailoring of the
waveforms to meet localized requirements.
• SDR technology and systems have been developed for military
applications.
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Cognitive Radio-Enabling Technologies
• Cognitive radio is a concept for the realization of smart and
advanced wireless systems.
• CR supports context awareness such as spectrum, location,
environment, waveform, power and infrastructure awareness.
• CR studies focuses on spectrum awareness capability.
• Location information has been traditionally used for
positioning systems and location based services (LBS).
• LBS can be utilized for different applications and solving
some issues in wireless networks.
• The applications based on utilization of location information
can be folded under four categories
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Cognitive Radio-Enabling Technologies
• The applications based on utilization of location information
can be folded under four categories
- LBS
- Network optimization
- Transceiver optimization
- Environment identification
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Cognitive Radio-Enabling Technologies
• Location and environment awareness engines consist of
sensing, awareness core and adaption systems respectively
similar to the location and environment awareness cycle of
creatures in the nature.
• Location and environment awareness engines receive tasks
from cognitive engine and they report back the results to the
cognitive engine for achieving goal driven and autonomous
location and environment aware application.
• Both engines can utilize various sensors and adaptive
waveform generator and processor capabilities of cognitive
radio to interact with and learn the surrounding environments.
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Cognitive Radio-Enabling Technologies
• Environment awareness engine senses the environmental
parameters and provides the values to the location awareness
engine.
• Spectrum awareness engine senses the spectrum and provides
the spectrum information (available bandwidth) to the location
awareness engine.
SENSING INTERFACE:
• Composed of two main components:
• Sensors
• Associated data post processing methods
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Cognitive Radio-Enabling Technologies
• Sensors are utilized to convert the signals acquired from
environment to electrical signals so that cognitive radios can
interpret.
• Acquired signals can be in different forms: electromagnetic,
optic and sound
• Sensors can be categorized in 3 types:
• Electromagnetic sensors
• Image sensors
• Acoustic sensors
• Corresponding data post processing algorithm for each
sensing technique is different.
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Cognitive Radio-Enabling Technologies
Classification of sensing mechanism in cognitive radio:
• 3 main categories based on type of sensors
• Radiosensing – sensing technique utilizes electromagnetic
sensors and the associated post processing scheme.
• Radiovision – sensing approach using image sensors and the
corresponding post processing scheme.
• Radiohearing – Employs acoustic sensors and the associated
post processing scheme
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Cognitive Radio-Enabling Technologies
LOCATION AWARENESS ENGINE:
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Cognitive Radio-Enabling Technologies
• The model consists of the following main subsystems
• Location sensing
• Location awareness core
• Adaptation of location aware systems
Location sensing:
• Estimate the location information of the target object in a
given format.
• The format of location information that needs to be sensed can
have significant effects on the complexity of location aware
algorithms.
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Cognitive Radio-Enabling Technologies
• CR can retrieve physical position of the remote computer from
the virtual position along with additional information.
• Mapping virtual position to the corresponding physical
position already exists.
• Extracting physical position of a remote device from its virtual
position information can be useful for cognitive radio.
• This information can be used to develop efficient location
assisted routing protocol
• Physical position of an object can be either absolute or relative
• Absolute position: Refers to complete coordinate knowledge
of an object
• Relative position: Position of an object relative to another or
neighbor objects that do or do not know their absolute
positions
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• CR device can estimate its absolute position using its relative
position along with absolute position of the reference device
that is used during relative positioning.
• Absolute position estimation techniques are more mature and
widely used compared to relative position estimation methods.
• Depending on accuracy requirements CR can switch between
absolute and relative position estimation methods.
• The absolute and relative position of the cognitive radio can be
quantified using coordinate systems
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Cognitive Radio-Enabling Technologies
• There are numerous global, continental and country specific
reference coordinate system for absolute position of an object
such as NAD(North American Datum), ED50(European
Datum), TD(Tokyo Datum), ECF(Earth Centered Fixed),
WGS(World Geodetic systems).
• Relative position information can be classified under three
groups of reference coordinate systems:
• 1-Dimentional: provides the location of a cognitive radio in a
single axis.
Eg. Distance between transmitter and receiver or distance
between two cognitive radio
• 2-dimentional: Provides the information of a cognitive radio
in a plane(x,y)
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• 3-dimentional: Provides the location of cognitive radio in
three dimentions(x,y,z)
• Eg. Cognitve wireless networks have the capability to estimate
3-D location of a cognitive radio node. Time parameter can be
included.
RADIO SENSING METHODS:
• Antenna based position sensing algorithms are extensively
used for wireless positioning systems.
• They are categorized under three groups:
• Range based schemes
• Range-free schemes
• Pattern matching-based schemes
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• Antenna based position estimation methods do not have
cognition capabilities that cognition radio requires.
• Realization of antenna-based position sensing technique with
cognition capabilities are done with the help of cognition
positioning systems.
RADIO VISION METHODS:
• Image sensors are used for visual position sensing methods.
• The position of the observer is estimated based on the images
acquired from the image sensors.
• Relationship between video camera mounted to the user and
cognition engine in cognitive radios resembles the relationship
between eye and brain in the human body.
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• The acquired images can be processed using advanced digital
image and signal processing techniques.(Eg. Pattern analysis
and machine intelligence algorithms.
• Scene state are in desired formats: text, image, video and
voice.
• A well known visual position sensing techniques is scene
analysis.
• Scene analysis is a pattern matching based position sensing
technique similar to RF pattern matching based
methods(eg.RF fingerprinting)
• Acquired images are used as patterns in scene analysis
whereas channel statistics are used as patterns in the RF
pattern matching based methods.
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• Main objective of Radio vision method:
• Requirement of image database
• Extensive image processing power
• Compared to robotics and computer systems, implementing
radio vision techniques such as cognitive vision systems is a
challenging tasks due to low power, cost and size constraints.
RADIO HEARING METHODS:
• Radio hearing based position sensing methods utilize acoustic
sensors for interaction with environments
• Implemented using 3 group of schemes:
• Range based
• Range-free
• Pattern matching based technique.
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• Cognitive radio realizes advanced radio hearing based position
sensing techniques functioning similar to Bat echo position
systems.
• Cognitive radio can acquire sound signal and use it as a
pattern.
• In addition, it can even calculate the spectrum of the captured
sound pattern and compare it with the sound pattern stored in
the database to infer its position.
• Different radio hearing based position sensing methods using
these three approaches are developed.
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LOCATION AWARENESS CORE:
• Main objective is to perform critical tasks related to location
information such as learning, reasoning and making decisions.
The core has the following functionalities:
• Seemless positioning and interoperability
• Security and privacy
• Statistical learning and tracking
• Mobility Management
• Location aware applications
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SEEMLESS POSITIONING AND INTEROPERABILITY:
• It is defined as a system that can keep the position accuracy at
a predefined level regardless of the changes in channel
environment.
Two Approaches:
• Waveform based methods
• Environment sensing based methods
Waveform based methods:
• Based on utilization of appropriate waveform or technology
depending on the user requirements and environment.
• Support all predefined waveforms of the existing and future
positioning systems and waveform switching mechanisms
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• Eg. European space project
• The SPACE prototype consists of the existing positioning
waveforms, algorithms and sensors such as GPS, UWB,
WLAN, Bluetooth…
• Depending on the user requirements and environments, most
appropriate positioning system is selected.
Environment sensing based methods
• Does not require multiple waveforms
• It is based on sensing channel environment parameters(eg.path
loss coefficient) and adapt the positioning algorithm in real
time.
• RSSI(Received signal strength Indication) based location
estimation algorithm for unknown channel environment is a
good example.
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Cognitive Radio-Enabling Technologies
• IEEE defines interoperability as the ability of two or more
systems or components to exchange information and to use the
information.
Interoperability issues in CR is grouped under two categories:
• Cognitive radio - Cognitive radio interoperability
Both cognitive radios can have same or different waveforms
They can exchange the information directly
• Cognitive radio – legacy radio interoperability
• Both needs to agree on one of the waveforms in order to
communicate
• Cognitive radio can switch its waveform to the waveform of
legacy radio, since legacy radio does not have
reconfigurability features.
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Security and Privacy:
• Extensive utilization of location information in cognitive radio
and networks has these two issues.
• Of many potential threats, tracking the position of a cognitive
radio user without authorization and adversarial attacks are
the two main ones.
• Without authorization can violate the user privacy
• Adversarial attacks can result in catastrophic scenarios since
LBS highly depend on the location information
• It is crucial to develop effective solutions to address these
issues.
• For instance, local or global geolocation privacy protection
methods can be developed to address privacy issues.
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Statistical learning and tracking :
• Location awareness engine have the capability to track mobile
CR users and it can be trained by the tracking data using
statistical learning tools such as neural networks and Markov
models to form user location profiles
• These profiles are used to predict the trajectory of CR users
and improve the positioning accuracy, especially in pattern
matching based positioning methods.
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Cognitive Radio-Enabling Technologies
Mobility Management:
• Utilization of location information in cognitive radios and
networks for different applications will have major impact on
system complexity.
• System capacity and implementation cost can be affected by
introduction of additional services and applications into
cognitive wireless networks.
• It is desirable to develop an accurate mobility model during
the network planning phase.
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Cognitive Radio-Enabling Technologies
Adaptation of location aware system:
• Main objective of adaptation block is to support location
awareness engine in terms of adaptation of algorithms and
parameters for the satisfaction of the user.
• The reported performance parameter or requirement of
location aware applications is accuracy, integrity, continuity
and availability.
• Range accuracy is one of the most important performance
parameters
• Indoor positioning systems demand higher precision accuracy
compared to outdoor positioning systems.
• In industrial areas which is a local positioning application,
typically 0.05 – 30m accuracy is obtained.
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Cognitive Radio-Enabling Technologies
ENVIRONMENT AWARENESS ENGINE:
• Environment awareness is one of the most substantial and
complicated task in cognitive radios.
• Creatures with environment awareness capabilities such as
human being and bats can be considered as models for the
realization of environment awareness in CR.
• Human being has different senses such as observing and
learning the surrounding environment and bats utilize their
echo position systems for object and environment
identification, target detection and tracking.
• Similar environment awareness techniques can be developed
for CR’s
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ENVIRONMENT AWARENESS ENGINE:
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Cognitive Radio-Enabling Technologies
ENVIRONMENT AWARENESS ENGINE:
• The model consists of environment awareness core,
topographical information , object recognition and tracking,
propagation characteristics, meterorological information,
environment sensing and environment adaptation.
• For wireless systems, an environment mainly consists of the
following entities: topographical information, objects,
propagation characteristics and meteorological information.
TOPOGRAPHICAL INFORMATION:
• Topography is defined as the science or practice of describing
a particular place, city, town, parish or tract of land or accurate
and detailed delineation and description of any locality.
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Cognitive Radio-Enabling Technologies
• Topology of a local region provides information about not only
the relief but also vegetation , human made structures, history
and culture of that particular area.
• Numerous advanced LBS can be developed.
• Eg. Google Map TM
• Here a mobile user (tourists) points the embedded camera
towards the point of interest and captures the image.
• The captured image is transmitted to the server to extract the
information related to the image and then send this information
to the mobile user.
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OBJECT RECOGNITION AND TRACKING:
• Objects are defined as human made entities present in the
target local environment temporarily or permanently.
• Large and permanent human made structures such as buildings
and bridges are considered as part of topography of
environment.
• Such structures are included in topographical information.
• Relatively small and movable human made entities such as
vehicles, home and office appliances are considered as objects.
• Object detection, identification and tracking are important
features of environment awareness engine.
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Cognitive Radio-Enabling Technologies
• Cognitive radar is introduced with the capability of target
detection and tracking.
• It has the capability of encompassing the transmitter,
environment and receiver.
PROPAGATION CHARACTERISTICS:
• Provides information on the characteristics of signal
progression through a medium(Channel environment)
• Propagation characteristics of channel environment shows ,
how the channel affects the transmitted signal.
• Statistical characteristics of wireless channel are categorized in
two groups:
• Large scale
• Small scale
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Cognitive Radio-Enabling Technologies
• Large scale statistics provide information on path loss
behavior of channel environment.
• Small scale statistics determine the drastic variations of
received signal in time and frequency due to short
displacements.
METEOROLOGICAL INFORMATION:
• It provides information on the weather of target local region,
which can affect the signal propagation.
• The current and future weather parameters such as rain , snow,
temperature, humidity and pressure can be acquired by either
using radio auxiliary sensors or from central cognitive base
station.
• By having current and forecasted meteorological information,
cognitive radio can adapt itself.
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Cognitive Radio-Enabling Technologies
• One of the performance parameters that can be affected from
rain is the carrier-to-interference ratio(C/I) and the
performance metric depends on the rain intensity of the
position of desired signal path and interferer signal path.
• If cognitive radio or network has a capability to acquire rain
intensity of local regions from a central meteorological server
or internet, then C/I adaptation can be performed.
• The main task of environment awareness engine in CR can be
summarized as acquiring the information on topography,
objects, propagation channel and meteorology of the target
local region and provides these information to other
components of CR used for other applications.
• For Eg. Object and environment identification, seamless
positioning and LOS-NLOS identification are three potential
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environment aware applications
RADIO FREQUENCY SPECTRUM AND
REGULATION
• Electromagnetic spectrum is our planet’s most valuable natural
resources
• SPECTRUM: Nature’s Communication highway:
• Radio frequency spectrum is an abundant natural resource that
uniformly covers the planet and is available for a wide variety
of useful purposes.
• Beyond the voice communications and increasingly dominant
multimedia and data networking, this spectrum is regularly
used for a diverse array of applications, including radar for
finding large and small objects(airplanes in sky, obstacles in
the vicinity, to studs in the walls of home), excitation for
illuminating spaces, monitoring and sensing applications.
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Physical Characteristics of spectrum
• Frequency is given by,
f=c/λ
• Time varying signal at a point in space is given by
S(t) = A(t) cos (2лft)
• S – signal strength
• t – time, A- Amplitude of the signal
• Like all elements of the electromagnetic spectrum, radio
frequency component of this spectrum has the wave like
characteristics of reflection, refraction, diffusion, absorption
and scattering.
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Implications for communication Applications
• Utility of the spectrum is derived from its ability to be
modulated in a variety of ways to transport useful information.
• This includes applying or removing power from a specific
frequency or spectral range such as pulse modulation or
amplitude shift keying(ASK), increasing or decreasing the
power level applied to a frequency, such as Amplitude
modulation(AM).
• Switching the transmitted power from one frequency to
another such as frequency modulation or frequency shift
keying(FSK)
• Altering the phase of the signal, phase shift keying(PSK)
• Combining these techniques variety of complex signal
encoding structures are created.
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Regulatory history and successes
• History of spectrum regulation closely followed the early
development and deployment of wireless communication
systems.
• First international wireless standards meeting, the international
Radiotelegraph conference was organized by International
Telegraph union(ITU), the governing body for wired telegraph
operation, held in Berlin in1906.
Objectives and Philosophy:
• ITU mission enables growth and sustained development of
telecommunications and information networks and to facilitate
universal access so that people everywhere can participate and
get benefit from emerging information society and global
economy.
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Regulatory history and successes
Early history and success:
• ITU became a united nations specialized agency.
• International Frequency Registration Board(IFRB) established
within the ITU to coordinate the increasingly complicated task
of managing the radio frequency spectrum.
• Frequency allocations, introduced in 1912 became mandatory
to assist, guide and tabulate spectrum use in various countries.
• In Unites States, primary regulatory body is Federal
Communications Commission(FCC).
• FCC develops and enforces regulations in support of the laws
governing the commercial use of the spectrum.
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Regulatory history and successes
• National Telecommunications and Information Administration
(NTIA) is a organization which reports its responsibility to the
department of Commerce in the executive branch of the
government.
• These bodies determine, maintain and regulate the
comprehensive allocation of US spectrum.
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Emerging Regulatory Challenges and Actions
Era of increasing Regulatory challenges
• To understand the dynamics behind market based direction,
supply of spectrum should be clearly finite, but demand for the
spectrum is fundamentally unbounded.
• This ever-increasing demand of spectrum is based on
“Quadruple whammy”.
• It is composed of four elements
(i) Applications:
• The number and variety of different radio applications are
virtually unbounded and rapidly evolving.
• For eg. Broadcast communications (Television, radio),
commercial communications(emergency services radio)
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Emerging Regulatory Challenges and Actions
• Industrial communications, aeronautical communications,
military communications, personal communications(cell
phone, two-way radios), wireless networks (personal, local
area, metropolitan), satellite communications.
(ii) Coverage:
• Need to offer the applications to an ever broader audience and
eliminate spatial constraints.
(iii) Duty Cycle:
• Most popular of these applications will be used for ever
increasing percentage of the time, following “always on
always connected”.
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Emerging Regulatory Challenges and Actions
(iv) Performance:
• These applications are used broadly and used all the time.
• The demands for ever-increasing levels of performance require
increasing allocation of spectral bandwidth, since there is
direct correlation between allocated bandwidth and the
sustained data rate that a channel can support.
• The simplest form is described by Nyquist bandwidth formula,
• C = 2B( binary signals)
• C = 2Blog2(M) for multilevel signals
• Where C – capacity, B – Bandwidth,
• M – number of signal levels
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Emerging Regulatory Challenges and Actions
• Market based spectrum management is leading to the
establishment of quasi-autonomous entities to manage national
communications resource with organizations as the office of
communications in the United Kingdom .
• The principal duty of Ofcom, in carrying out their functions:
• To further the interests of citizens in relation to
communications.
• To further the interests of consumers in relevant market, by
promoting competitions.(This organization is responsible for
regulating television, radio, telecommunications and various
wireless services)
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Emerging Regulatory Challenges and Actions
Regulatory Actions:
• This trend is to enable temporal as well as spatial spectrum
sharing in US in 1985.
• US FCC modifies its rule for the industrial, scientific and
medical band to enable its use for wireless communication.
• This was the first major initiatives to begin to address this
critical issue.
• Other one is the allowance of ultra-wideband(UWB).
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Emerging Regulatory Challenges and Actions
Spectrum task forces and Commissions:
• The years of variety of task forces have focused on spectrum
usage.
• In US these have been commissioned by organizations like,
• The Federal communications commission(FCC)
• The
national
Telecommunication
and
Information
administration(NTIA)
• The national research council for the national academies
• The national science foundation and congress
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Regulatory Issues of Cognitive Access
• The regulator could adopt a variety of approaches to cognitive
access,
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Regulatory Issues of Cognitive Access
• The geographic areas between towers where the spectrum has
not been licensed to anyone is called white space.
• This unowned spectrum can be treated differently because
there is no owner.
• Different regulatory decision might be expected for such a
spectrum compared to a spectrum that has been regionally or
nationally licensed.
Regulatory Implications of different Methods of Cognition:
• Three broad techniques whether bands are free from use, they
are,
• Sensing
• Beacons
• Geolocation
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Regulatory Issues of Cognitive Access
• These can be used discretely or in combination to effect the
desired level in the attainment of a low-interference
environment.
Geographical Databases:
• Used in alternative for sensing in cognitive device to precisely
know its location and have access to a database listing the
frequencies allowed to use in each location.
Beacon Reception:
• Requires the transmission of a signal from appropriate
infrastructure providing information on which frequencies are
available for cognitive use in vicinity.
• Cognitive devices tune to this channel and use the information
provided to select their preferred frequency.
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Regulatory Issues of Cognitive Access
• Regulatory developments to date:
• FCC concluded that at present sensing alone would result in an
unacceptable risk of interference.
• It further concluded that geographical databases were also
required.
• Specific details such as locational accuracy and the frequency
of consulting the database were also stipulated in the report.
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Spectrum measurements and usage
Early spectrum occupancy studies:
• Spectrum occupancy studies of various kinds have taken for
many years.
• These resolve into the following three categories:
• Short term “Snapshot studies”
• Long term “Spectrum Observatory” studies
• Sensor array studies
Snapshot studies:
• These studies demonstrated that there is abundance of unused
or lightly used spectrum, which could be exploited through use
of dynamic spectrum access networks or even static networks
with carefully defined geographic boundaries.
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Spectrum measurements and usage
Spectrum Observatory:
• Spectrum observatory is a relatively new idea in the spectrum
world.
• Snapshot studies focused on relatively narrow spectral band.
• These are very effective in gaining general spectral
information about location.
• First true spectrum observatory is the wireless Network and
Communications research Centre (WiNCom) observatory
funded by NSF, located at IIT Chicago
• One of its fundamental purpose is to detect and characterize
spectral holes in time and space that can be exploited by
cognitive radio systems.
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Spectrum measurements and usage
Spectral Sensor Arrays:
• Sensor arrays should become a very valuable measurement
tool.
• Major program focused in this area is the European Union’s
Seventh Framework collaborative projects.
• This sensor array system would be particularly valuable in
high network traffic areas and even greater values in areas that
have dynamic usage pattern(eg. Mobile, automobile based
wireless systems.
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EC 8071- COGNITIVE RADIO
UNIT II: COGNITIVE RADIO ARCHITECTURE
Cognition cycle – orient, plan, decide and act
phases, Organization, SDR as a platform for
Cognitive Radio – Hardware and Software
Architectures, Overview of IEEE 802.22
standard for broadband wireless access in TV
bands.
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INTRODUCTION
• Structural design for cognitive radio consists of the functions,
components and design rules to support the evolution of
cognitive radio.
• The architecture integrates the contribution of researches on
specific disciplines of software radio, network engineering,
natural language processing and machine learning.
• The architecture minimizes the dependence on knowledge
engineering through the integration of machine learning.
• This minimizes the burden on the user through the integration
of natural language processing
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COGNITION CYCLE
• The architecture comprises of a set of design rules by which
cognition level of information services is achieved by a set of
components which supports cost-effective evolution of
increasingly capable implementations over time.
• Cognition subsystem includes
• an inference hierarchy
• temporal organization
• flow of inferences
• control states
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OOPDA Loop
COGNITION CYCLE
Fig. Simplified Cognition cycle
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COGNITION CYCLE
• This cycle implements the capabilities in a reactive sequence.
• Stimuli enter the CR as sensory interrupts, dispatched to the
cognition cycle for a response.
• Such cognitive radio continually observes (senses and
perceives) the environment, orient itself, creates plan, decides
and then acts.
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Epochs
• Wake epoch
• Primary reasoning activities- reactive to the environment
• Receipt of a new stimulus – new primary cognition cycle
• Machine learning – computationally intensive
• Cognitive radio – sleep and prayer epoch – support
machine learning
• Sleep epochs
• Power down conditions
• Long time period – not in use – sufficient electrical power
for processing.
• CR-ML algorithms without detracking from its ability.
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Epochs
• Dream epoch
• Computationally intensive pattern recognition and learning.
• Prayer epoch
• Interacting with a higher authority – network infrastructure
• Learning opportunities – not resolved in sleep epoch –
brought to prayer epoch.
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Observe(Sense and perceive)
• Cognitive observes environment
• By parsing incoming information streams
• Monitoring and speech-to-text conversion of radio broadcasts
• Any RF LAN or other short range wireless broadcasts.
Observation phase:
• Reads Location , temperature and light level sensors
• Infer user’s communications context
• Senses and perceives the environment
• Multiple stimuli + binding – generate plans for action
• Cognitive radio aggregates experience – compares prior
aggregates to the current situation.
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Observe(Sense and perceive)
• Novelty detector – new stimuli
• Partially familiar stimuli- identify incremental learning
primitives.
• Observe phase – User Sensory perception + environment
sensor subsystems
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Orient
Orient phase:
• Determines the Significance of observation by binding the
observation – previously known set of stimuli.
• Orient phase contains,
• Internal data structures
• Short term memory(STM)
• Long-term memory (LTM)
• Natural environment – information redundancy –STM to LTM
• Cognitive radio transfer from STM to LTM – sleep cycle –
analyzed both internally and with respect to existing LTM.
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Orient
• Orient phase is the collection of activity in the cognition
component.
• Matching – Stimulus recognition and binding
Stimulus recognition
• Occurs when there is exact match – current stimulus and prior
experience
• The prototype is continually recognizing the exact matches
and recording the exact matches that occurred along with time
measured in the number of cognition cycles between the last
exact match.
• By default, the response to a given stimulus is to repeat the
stimulus to the next layer up the inference hierarchy for
aggregation of the raw stimuli.
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Orient
Binding:
• Occurs when there is nearly exact match - current stimulus and
prior experience
• Criteria for applying prior experience to the current situationmet
• Binding is the first step in generating a plan for behaving in
the given state similar to the last occurrence of the stimuli.
• Determines – priority associated with the stimuli
• Better binding – higher priority for autonomous learning
• Less effective binding – lower priority for the incipient plan
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Plan
• Stimuli are dealt- deliberatively rather than reactively
• An incoming network message – generate a plan(“Normal
Path”)
Planning includes,
• Plan generation
• Formal models of causality – embedded into planning tools
• Include reasoning about time
• Reactive responses – preprogrammed – other behaviors
planned
• Stimulus associated – simple plan – as a function of planning
parameters + planning system
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Plan
Open source planning tools:
• Embedding of planning subsystems into the CRA
• Enhance the plan component
• Enable synthesis of RF and information access behaviors in a
goal oriented way based on perceptions(visual, audio, text) and
rules and previously learned user preferences.
DECIDE:
• Selects – candidate plan
• CR- have choice
• Alert user to an incoming message(behave like a pager)
• Defer interruption until later(screening calls during an
important meeting)
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Act Phase
Acting Phase
• Initiates- selected processing using effector modules
• Effectors may access– external world / CR’s internal states
Externally oriented Actions:
• Access to the external world consists of composing messages
to be spoken into local environment or expressed in text form
locally or to another CR or CN using,
• Knowledge Query and Manipulation language(KQML)
• Radio knowledge Representation language(RKRL)
• Web Ontology Language(OWL)
• Radio eXtensible Markup Language(RXML)
• Appropriate knowledge interchange standard
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Act Phase
Internally Oriented Actions:
• Actions on internal states include – machine controllable
resources
• CR can affect the contents of -Internal models – adding a
model of stimulus-experience-response(serModel) to the
existing internal model structure.
• Multiple independent sources of the same concept in a scene
reinforce that concept for that scene.
• Experience – reactively integrated into RXML knowledge
structures.
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Learning
• Function – perception, observation, decisions and actions
• Initial Learning:
• Observe phase perception hierarchy – which is continuously
matched against all prior stimuli to continually count
occurrences and to remember time since last occurrences of
the stimuli from primitives to aggregates.
• Introduction of new internal models
• Existing models and case based reasoning(CBR) bindings
• Many opportunities to Integrate – ML into CRA
• Learning mechanism
• New type serModel
• Internally generated serModel
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Self monitoring timing
• CR is inherently self-referential and self modifying
• Such tools will emerge, assisted by the needs of CR and the
architecture framework of cognitive cycle.
• Prior phases – computational structures – execution time
advance
• Each phase – restrict its computations – pre-computed upper
bound
• Architecture – prohibitions + data set requirements – degree of
stability
• Unnatural act – next generation compilers + CASE tools
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Self monitoring timing
Cognition cycle
• No internal loops
• Each iteration – defined amount of time
• Amount of computational work done within the cycle will
increase but no conditions should explicit or implicit loops be
introduced into the cognition cycle that would extend it
beyond a given cycle time.
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Retrospection & Reaching Out
Retrospection
• Assimilation of knowledge by ML can be computationally
intensive.
• CR has - Sleep and prayer epoch – support ML
• Sleep epoch – long period of time, CR will not be in use, has
sufficient electrical power for processing.
• Run ML algorithm – without detracting from its ability to
support its user needs.
• ML algorithm – integrate experience by aggregating statistical
parameters.
• Sleep cycle – may re-run Stimulus – response sequence + with
new learning parameters
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Retrospection & Reaching Out
• fitness landscape- improves decision parameters from recent
experience.
Reaching Out
• Learning opportunities not resolved in sleep epoch
• Attention of the user, host network or designer – prayer epochs
• Prayer epoch – complex problems to an infrastructure support.
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Architecture of cognitive radio
implemented via cognition cycle
• Cognition functions implemented via cognition components
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Architecture of cognitive radio
implemented via cognition cycle
• Data structures include the reinforced hierarchical sequences
words, phrases, dialogs and scenes of the observe phase.
• Novel sequence represent the current stimulus-response cases
of the cognitive behavior model.
• Known sequence consists of RKRL statements embedded in
PDA
• Nearest sequence is the known sequence.
• World model, S consists primarily of bindings between a prior
data structure and current scene.
• These structures are associated with the observe phase.
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Functions of cognitive radio
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Functions of cognitive radio
• Functional component is a black box to which functions are
allocated
• User interface functions include optimized hardware.
• Functional components are,
• User sensory perception(SP), includes acoustic, video sensing
and perception functions.
• Local
environment
sensors
(location,
temperature,
accelerometer,…)
• System applications – media independent services such as
network game
• SDR functions – RF sensing , SDR applications
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Functions of cognitive radio
• Cognition functions – system control, planning, learning
• Local effector functions – speech synthesis, text, graphics and
multimedia displays
• Primary radio cognition functions consists of:
• Recognize user communication context:
• Cognition function should rely on processing streams of user
interaction with applications as its primary means.
• Mediate wireless information services as a function of
context:
• Continuously track the parameters of wireless networks present
in the environment.
• Parameters include spectrum occupancy, receive signal strength
as a function of time and space, available QoS, related costs.
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SDR as a platform for cognitive radio
• This explores both the hardware and software domains.
• Hardware is analyzed in terms of its capabilities.
• Radio frequency font end(RFFE) can transmit up to a certain
frequency
• Software is generally treated as an enabler.
• Cognitive radio assumes that system hardware and software
infrastructure is capable of supporting the flexibility demanded
by cognitive algorithms.
• It provides significant flexibility with a series of tunable
hardware components that are in direct control of cognitive
software.
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SDR as a platform for cognitive radio
• Cognitive system can support large number of protocols and air
interfaces.
• It is desirable to have a generic hardware structure.
• Series of generalized computing structures implies that the
cognitive engine must contain hardware specific knowledge.
• Cognitive engine can navigate different optimization strategies.
• So change in the underlying hardware would require a change
in the cognitive engine knowledge base.
• This problem increases when we consider porting the engine to
other radio platforms.
• For eg. There could be a research and development platform
used in testing a variety of cognitive algorithms.
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SDR as a platform for cognitive radio
• These algorithms are deployed in systems.
• Cognitive engine is used in the deployed system’s management
structure.
• Cognitive engine could be modified to support new hardware
platform.
• SDR is a methodology for the development of applications in a
consistent and modular fashion in both hardware and software
components and can be reused from implementation to
implementation.
• It also provides the management structure for the description,
creation and waveforms.
• SDR supports RF and intermediate frequency hardware that is
necessary to interface the computing hardware with radio
signals.
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Hardware Architecture
• Goal: Explore hardware for SDR from a radio standpoint.
• The figure shows the basic radio receiver.
• The generic architecture traces from the antenna through the
radio and up the protocol stack to the application.
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Hardware Architecture
RF Externals:
• Many radios achieve satisfactory performance with an antenna to yield a
beam pattern.
• Antenna used over a wide frequency range requires antenna tuner to
optimize VSWR, radio efficiency.
• Each time transceiver change frequency, antenna tuner will be informed.
• Many of these antennas update their steering angle as rapidly as once every
millisecond.
• Sophisticated antenna is MIMO antenna.
• The interface boundary between the radio and the antenna is blurred by the
wide bandwidth and complex interfaces between the beam steering signal
processing , large number of parallel RF front end receivers and final
modem signal processing is used.
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Hardware Architecture
• Another external component is RF power amplifier(PA).
• Power amplifiers transmit when the transceiver is in the
transmit mode and stop transmitting when the transceiver is in
the receive mode.
• Low noise amplifier (LNA) normally have a tunable filter with
it.
RF FRONT END
• It consists of the receiver and the transmitter analog functions,
frequency up converters and down converters, filters and
amplifiers.
• The front end design will maximize the dynamic range of
signals that the receiver can process, through automatic gain
control(AGC).
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Hardware Architecture
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Hardware Architecture
Analog to digital converters:
• The digital receiver industry is looking for wider bandwidth
and greater dynamic range.
• Successive approximation ADC were replaced by flash
converters and now replaced with sigma-delta ADC.
• ADC can provide 105Msps at 14 bit resolution.
• Special purpose ADC provide sample rates over 5G samples
per second at 8 bit resolution.
MODEM:
• After down conversion, filtering and equalization, the symbols
are converted to bits by a symbol detector/demodulator
combination which may include matched filter.
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Hardware Architecture
• A symbol is selected that most closely matches the received
signal.
• The bits that are represented by the symbol are then passed to
forward error correcting function to correct bit errors.
• The received and error corrected bits are parsed into various
fields of message, header, address, traffic…
• The message fields are then examined by the protocol layers
eventually delivering messages to an application.(eg. Web
browser, voice coder) thus delivering the function expected by
the user.
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Hardware Architecture
• Forward error correction:
• The demodulated bits are passed on to FEC stage for reducing
the bit errors.
• Medium Access Control:
• It generally includes framing information with frame
synchronization structures, MAC addressing , error detection,
link management structures with possible fragmentation /
defragmentation structures.
• Network layer is designed for end to end connectivity support.
• Network layer is passed to application layer which performs
user functions and interface(speaker/microphone, graphical
user interface, human-computer interface)
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Hardware Architecture
• User Application:
• User application may range from voice telephony, to data
networking, to text messaging, to graphic display, to live video.
• For voice telephony, dominant mode is to code the voice to a
moderate data rate.
• Data rates from 4800bps to 13000bps which provides excellent
voice quality and low distortion.
• Voice coding applications are implemented on DSP
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Hardware Architecture
•
•
•
•
•
Baseband processor engines:
Four basic classes of programmable processors are available:
GPP, DSP, FPGA and CCM
GPP:
Usually pipeline the arithmetic functions and decision logic
functions.
• Execute many instructions in parallel with arithmetic
computation, logic evaluations and branch decisions.
• GPP type processors is used for protocol stack processing.
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Hardware Architecture
•
•
•
•
•
•
•
•
DSP:
Signal processes can be performed at high speed.
DSP internal architecture is optimized to perform very fast.
They have one or more multipliers and one or more
accumulators in hardware.
DSP is much more efficient in signal processing but less
capable to accommodate the software associated with the
network protocols.
FPGA:
Capable of providing multiple accumulate operations on a
single chip
More than 100 accumulators are arranged to perform
accumulate processes at frequencies of more than 200MHz.
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Hardware Architecture
• FPGA provides timing logic to synthesize clocks, baud rate,
chip rate, time slot and frame timing leading to compact
waveform implementation.
• Uses VHDL for defining hardware architecture and
functionality.
• Baseband Processing deployment:
• Once a set of devices and algorithm performance for each of
the devices has been established, there is a finite set of
possibilities that can be optimized.
• Several algorithms exists for optimizing specific values such as
minimum mean square error(MMSE), maximum likelihood
estimation(MLE), genetic algorithms, neural nets or large set of
algorithms.
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Hardware Architecture
• Proposed methodology is partitioned into platform specific
and waveform specific analysis
• Platform specific analysis is partitioned into two types:
DSP/GPP and FPGA.
• Platform specific analysis is as follows:
• 1. Create an operations audit of the target algorithms (number
and type of operations)
• 2. For DSP
• (a) create a set of target devices.
• (b) Establish cycle saving capabilities of each target device.
• 3. For FPGA
• (a) Create a set of devices
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Hardware Architecture
• (b) Establish mapping between different FPGA families.
• (c) Find local device count for each target algorithm
• (d) Use mapping between devices to find appropriate target on
devices when benchmark is not available.
• It is also possible to create performance estimates for different
waveforms
• (a) Create block based breakdown of the target waveform using
target algorithms.
• (b) Breakdown target waveform into clock domains.
• (c) Estimate the time necessary to complete each algorithm.
• (d) Compute the number of operations per second(OPS) needed
for each algorithm.
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Hardware Architecture
• (e) Create a set of devices or combination of devices that meet
some broad criteria.
• (f) Attempt to map algorithm onto the given devices in set.
• For DSP,
• (a) Compute the number of operations per second(OPS) needed
for each algorithm.
• (b) The result of algorithm map is a MIPS(million instructions
per second) count for each device.
• FPGA,
• (a) Mapping of the algorithm of the number of occupied logical
devices.
• (b) Make sure that the clock domains needed for algorithm can
be supported by FPGA
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Hardware Architecture
• (c) Apply appropriate optimization algorithm which includes
power budgets and performance metrics.
• Multicore systems and System-on-chip:
• As technology reaches transistors under 100nm, the key
problems become the inability to continue as the power
dissipation is very high.
• Power consumption of an active circuit is given as,
• P = α C f V2
• α = switching activity, C = capacitance, f= clock speed, V =
operating voltage
• By reducing interface capacitance and voltage swing, system
efficiency increases.
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Software Architecture
• Software is designed to support baseband signal processing
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Software Architecture
• The stack starts with the hardware and one or more data buses
that move information among various processors.
• On top of the hardware, several standardized layers of software
are installed which includes(boot loader, operating system,
board support package and Hardware abstraction layer.
• Hardware abstraction layer(HAL) provides a method for GPP
to communicate with DSP and FPGA processors.
• US government has defined standardized software architecture
known as the software Communication Architecture(SCA)
• SCA is a core framework to provide a standardized process for
identifying the available computational resources of the radio,
matching those resources to the required resources for an
application.
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Software Architecture
• SCA is built upon a standard set of OS features. Which has
standardized APIs to perform OS functions such as file
management and computational task scheduling.
• SCA specifies a Common Object Request Broker
Architecture(CORBA), which provides a standardized method
for software objects to communicate with each other
• SCA provides a standardized method of defining the
requirements for each application, performed in extensible
markup language(XML).
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Software Architecture
• Design philosophies and patterns for software architecture:
• Software design is formalized into a variety of design
philosophies such as object oriented programming(OOP),
component based programming(CBP), aspect-oriented
programming(ASP)
• Linear programming(LP):
• It is a methodology in which the developer follows a linear
thought process for the development of the code
• The process follows a logical flow
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Software Architecture
•
•
•
•
•
•
•
•
•
Execution of a function involves:
Swapping of the stack
Changing the context of operation
Performing the function’s work
Returning result to the calling function
Object oriented programming:
Extends the data structure concept to describe the whole object.
An object is a collection of member variables and functions
A class is an object type and an object is a specific instance of a
particular class.
• Several languages are OOP languages eg. Java, C
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Software Architecture
• Component based programming:
• It is an extension of OOP concept.
• An object is basic unit which comprises of one or more classes
and is completely defined by its interfaces and functionalities.
• A component could be a computer, where the computer
component is defined as the collection of keyboard, mouse,
display and actual computer case.
• The nature of the computer is irrelevant to the user as long as
interfaces and functionality remains the same.
• The primary goal of CBP is to create stand-alone components
that can be easily interchanged between implementations.
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Software Architecture
• Aspect oriented programming:
• Allows for the creation of relationship between different
classes.
• AOP requires creation of new language constructs that can
associate an aspect to a particular class.
• There are several languages(AspectJ, AspectC, Aspect#)
• Design Philosophy and SDR:
• Dominant philosophy in SDR design is CBP, use of separate
components for the different functional blocks of a radio
system such as link control or network stack
• SDR is relatively new discipline with few open implementation
examples.
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Software Architecture
•
•
•
•
•
•
Design patterns:
These are programming methodologies that a developer uses.
Patterns provide two principal benefits:
They help in code reuse
They create a common terminology
Where common terminology is important when working on
teams because it simplifies communications between team
members.
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OVERVIEW OF IEEE 802.22 STANDARD
• The Wireless regional network is expected to operate in lower
population density areas and provide broadband access to data
networks in VHF and UHF bands in the range of frequencies
between 54MHz and 862MHz.
• In 802.22 functional requirement, the capacity at the user
terminal is expected to be of 1.5Mbps in the downstream and
384Kbps in the upstream
• Prominent target application of 802.22 WRAN(Wireless
regional area network) is a wireless broadband access in rural
and remote areas with performance comparable to those of
existing fixed broadband access technologies(DSL and cable
modems) serving urbans and suburban areas.
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Reference Architecture
• 802.22 system specifies a fixed point to multipoint(PMP)
wireless air interface whereby a base station(BS) manages its
own cell and all associated consumer premise equipment(CPE)
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Reference Architecture
• The base station(BS) controls the medium access in its cell and
transmits in the downstream direction to various CPE which
respond back to the BS in the upstream direction.
• 802.22 system follows a strict master/slave relationship ,
wherein BS performs the role of the master and CPE are the
slaves.
• No CPE is allowed to transmit before receiving proper
authorization from a BS
• 802.22 BS manages a unique feature of distributed sensing.
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IEEE 802.22 physical layer
• 802.22 PHY layer is specifically designed to support a system
that uses vacant TV channels to provide wireless
communication access over distance of 100km.
• The PHY specification is based on orthogonal frequency
division multiple access(OFDMA) for both upstream(US) and
downstream(DS) access.
• Preamble, Control Header and MAP definition:
• In 802.22 allocation of resources in OFDMA frame can be
made in terms of sub-channels and symbols.
• Sub-channel is defined as a set of 28 contiguous OFDM
subcarriers and there are 60 sub-channels per symbol.
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IEEE 802.22 physical layer
• In the first frame of the super frame, the first symbol is the
super frame preamble followed by a frame preamble symbol.
• The third symbol is the super frame control header(SCH)
• Fourth symbol contains the frame control header(FCH)
• Frame length is 10ms.
• Preamble definition:
• Two types of frequency domain sequences are defined to
facilitate burst detection, synchronization and channel
estimation at 802.22 receiver.
• 1. Short training Sequence(STS):
• This sequence is formed by inserting a nonzero binary value on
every fourth subcarrier.
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IEEE 802.22 physical layer
• In time domain, this results in four repetitions of 512 sample
sequence in each OFDM symbol.
• 2.Long training sequence(LTS):
• The sequence is formed by inserting a non zero binary value on
every second subcarrier.
• In the time domain, this results in two repetitions of a 1024
sample sequence in each OFDM symbol.
• STS is used to form the super frame and CBP preambles, while
LTS is used to form frame preamble.
• The super frame preamble is used by the receiver for frequency
and time synchronization.
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Control header and MAP definition
• Here we define the structure of two control headers (SCH and
FCH) and the MAPs.
• SCH is transmitted using PHY mode 1 and TCP(transmission
control protocol) = 1/2TFFT
• Transmitted over all data subcarriers, encoded by a rate-1/2
convolutional coder and after interleaving is mapped using
QPSK constellation resulting in 336 QPSK symbols.
• The FSH is transmitted as part of the DS protocol data
unit(PDU) in the DS subframe and uses the basic data rate
mode.
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CBP packet format
• He first symbol is the preamble, followed by data payload1 and an optional
data payload 2.
• The length field of the first symbol enables a receiver to determine the
presence and absence of the second data symbol.
• The data symbols consists of the data and the pilot subcarriers.
• The payload is divided into blocks of 418 bits before encoding and
mapping.
• The encoded bits are then mapped using QPSK constellation
• QPSK symbols is transmitted on 3 sub-carriers to provide additional
frequency diversity.
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Channel coding and modulation schemes
• Channel coding includes data scrambling, convolutional coding or advanced
coding, puncturing , bit interleaving and constellation mapping.
• The frame payload data are first processed by the data scrambler using
pseudorandom binary sequence generator with the generator polynomial.
• FEC follows the data scrambler.
• Coding scheme in 802.22 is convolutional coding.
• Data burst is encoded using a rate-1/2 binary convolutional encoder.
• The input data to the mapper are first divided into groups of two for QPSK,
four for 16-QAM and six for 64-QAM
• Mapping is performed using gray coding constellation mapping.
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Channel coding and modulation schemes
• Transmit power control:
• TPC is an important feature in 802.22, since it requires only minimum
transmit power in maintaining link quality, which further enhances
incumbent protection in addition to spectrum sensing, databases and
geolocation
• IEEE 802.22 MEDIUM ACCESS CONTROL LAYER
• Super frame and Frame structures:
• 802.22 MAC uses a synchronous timing structure, where frames are
grouped into a super frame structure.
• Super frame preamble is used for time synchronization, while frame
preamble is used for channel estimation.
• SCH carries the BS MAC address along with the schedule of period of
sensing and other information about the cell.
• After SCH , BS transmits the frame control header(FCH) followed by the
messages within the first frame.
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IEEE 802.22 MEDIUM ACCESS CONTROL LAYER
• Remaining 15 frames within the superframe start with the frame preamble.
followed by the FCH and subsequent data message
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MAC frame structure
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IEEE 802.22 MEDIUM ACCESS CONTROL LAYER
• The frame is divided into DS and US subframes and the self coexistence
window(SCW), which is scheduled by BS at the end of the frame.
• The first downstream burst after the FCH is used to transmit DS/US MAPs.
The DS/US channel descriptor messages(DCD and UCD) and other MAC
protocol messages.
• DS/US MAPs are broadcast messages that specify the resource allocation in
the DS and US sub-frames.
• The DCD and UCD are usually transmitted by the BS at periodic intervals
to define the characteristics of the DS and US physical channels.
• After control signaling, BS can schedule the DS burst for data transmission
using different modulation/coding schemes for each burst.
• In the US subframe, BS can allocate resources for contention based access
before the data bursts which can be used for ranging, bandwidth requests
and urgent co-existence situation(UCS) notification.
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IEEE 802.22 MEDIUM ACCESS CONTROL LAYER
• UCS window is a new feature used by CPE to transmit an indication that an
incumbent signal is detected on the channel.
• BS may also reserve five symbols at the end of the frame for self coexistence window(SCW).
• SCW is used for execution of coexistence beacon protocol(CBP), which
involves transmission of coexistence beacons carrying information about the
cell and specific coexistence mechanisms.
• INCUMBENT DETECTION:
• Two important capabilities were introduced in the MAC layer to support
incumbent detection.
• Network quiet period:
• BS can schedule network wide quite periods (QP) during which all
transmissions are suspended and sensing can be performed more reliably.
• BS can schedule QP by using QP scheduling fields in SCH.
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IEEE 802.22 MEDIUM ACCESS CONTROL LAYER
• Channel measurement management:
• In case incumbent is detected by BS, BS takes appropriate steps to avoid
interference
• But when CPE detects an incumbent, it has to report to the BS.
• For that MAC layer includes channel measurement request and reports
messages which allows the BS to take full control of the incumbent
detection and notification.
• Synchronization:
• It is needed not only for communication purpose between and CPE but also
for incumbent protection.
• BS and CPE in a cell must be synchronized to ensure no transmissions
occur during the QP for sensing.
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IEEE 802.22 MEDIUM ACCESS CONTROL LAYER
• Self-coexistence:
• Unlicensed spectrum access model adopted for 802.22 systems ensures
efficient and fair spectrum utilization.
• Key elements:
• Neighboring network discovery and coordination
• Coexistence beacon protocol
• Resource sharing mechanisms
• Quality of service support:
• Several mechanism support QoS for upstream and downstream traffic
• Service flow QoS scheduling:
• Primary purpose of QoS supports at the MAC layer is to define the
transmission ordering and scheduling on the air-interface.
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IEEE 802.22 MEDIUM ACCESS CONTROL LAYER
• Activation model:
• Service flow can be classified as provisioned , admitted or active.
• To activate a service flow, BS maps the service flow to a CID(connection
identifier) which identifies the connection between CPE and BS across
which data is delivered.
• Service flow can also be in transient admitted state, where the resources are
not yet completely activated.
• Dynamic service establishment:
• MAC layer provides a series of management messages and procedures to
create, change or delete service flows.
• The DSA messages create a new service flow
• The DSC messages change an existing service flow
• The DSD messages delete an existing service flow
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EC 8071- COGNITIVE RADIO
UNIT III SPECTRUM SENSING AND DYNAMIC
SPECTRUM ACCESS
Introduction – Primary user detection techniques –
energy detection, feature detection, matched filtering,
cooperative
detection
and
other
approaches,
Fundamental Tradeoffs in spectrum sensing, Spectrum
Sharing Models of Dynamic Spectrum Access Unlicensed and Licensed Spectrum Sharing,
Fundamental Limits of Cognitive Radio.
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INTRODUCTION
• Due to the rapid advance of wireless communication, digital
communication exists in licensed and unlicensed bands
suitable for different demands and applications such as
GSM/GPRS, IEEE 802.11, Bluetooth, UWB, Zigbee, 3G LTE,
IEEE 802.16
• Radio propagation favors the use of spectrum under 3GHz due
to non-line of sight propagation.
• In the past, spectrum allocation was based on the specific band
assignments designed for a particular service.
• There is a dramatic increase in the access to limited spectrum
for mobile services and applications.
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INTRODUCTION
• Dynamic spectrum access is proposed as a solution to these
problems of current inefficient spectrum usage.
• The inefficient usage of the existing spectrum can be improved
through opportunistic access to the licensed bands by the
existing users(primary users).
• Cognitive radio technology provides the capacity to share the
wireless channel with the licensed users in an opportunistic
way.
• CR are envisioned to be able to provide high bandwidth to
mobile users via heterogeneous wireless architectures and
dynamic spectrum access techniques.
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INTRODUCTION
• In order to share the spectrum with licensed users and to meet
the diverse quality of service requirement of applications, each
CR user in the network must:
• Determine the portion of spectrum that is available known as
spectrum sensing.
• Select the best available channel, called spectrum decision.
• Coordinate access to the channel with other users known as
spectrum sharing.
• Vacate the channel when a licensed user is detected, referred
to as spectrum mobility.
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INTRODUCTION
• CR has the capability of being cognitive, reconfigurable and
self organized to fulfill the functions of spectrum sensing,
spectrum decision, spectrum sharing and spectrum mobility
• CAPABILITIES OF COGNITIVE RADIOS:
• Cognitive radio enables the improvement of the spectrum use
in a dynamic manner.
• It is an intelligent wireless communication system that is
aware of its surrounding environment and uses the
methodology of understanding by building to learn from the
environment and adapt its internal states to statistical
variations in the incoming RF stimuli by making changes in
operating parameters in real time.
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INTRODUCTION
Cognitive radio shall
• Sense the environment (cognitive capability)
• Analyze and learn sensed information(Self-organized
capability)
• Adapt to the environment(reconfigurable capabilities)
• We summarize cognitive capability
• Spectrum sensing:
• CR can sense spectrum and detect spectrum holes , those
frequency bands not used by licensed users.
• CR could incorporate sharing of the spectrum under the
agreement between a licensee and a third party.
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INTRODUCTION
• Location identification:
• It is the ability to determine its location and the location of
other transmitters and select appropriate operating parameters
like power and frequency.
• Network/system discovery:
• It shall discover available networks around it.
• The networks are reachable either via directed one hop
communication or multi-hop relay nodes.
• Service discovery:
• It accompanies network/system discovery.
• Network/system operators provide their services through their
access networks.
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Reconfigurable Capability of CR
• Frequency Agility:
• Ability of a radio to change its operating frequency.
• This method usually combines with a method to select
operating frequency based on the sensing of signals from other
transmitters.
• Dynamic frequency selection:
• Dynamically detects signals from other radio frequency
systems and avoids co-channel operation with those system.
• Adaptive modulation/coding(AMC):
• It is developed to approach channel capacity in fading
channels.
• It can modify transmission characteristics and waveforms to
provide improved spectrum access
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Reconfigurable Capability of CR
• Transmit power control(TPC):
• Enables the device to switch dynamically between several
transmission power levels.
• It allows transmission at the allowable limits and reduces the
transmitter power to a lower level to allow greater sharing of
spectrum.
• Dynamic system/network access:
• For cognitive radio terminal to access multiple communication
systems/networks that run different protocols, the ability to
reconfigure itself to these systems is necessary.
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Self organized capability:
• CR should be able to self-organize their communication based
on sensing and reconfigurable functions.
• Spectrum/radio resource management:
• A good spectrum management scheme is necessary to manage
and organize effectively spectrum holes .
• Mobility and connection management:
• It helps in neighborhood discovery, detect available internet
access and support handoffs which helps CR to select route
and networks.
• Trust/Security Management:
• Trust is a prerequisite for security operations to support
security functions in dynamic environments.
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Primary user signal detection techniques
• Spectrum sensor performs a binary hypothesis test, whether or
not there are primary signals in a particular channel.
• The channel is idle under null hypothesis and busy under the
alternate
• Under idle scenario, the received signal is essentially the
ambient noise in RF environment.
• Under busy scenario, received signal consists of primary user
signal and the ambient noise
• W(k) – represents ambient noise , s(k) - primary user signal
• k = 1,2,3…n, where n is number of received samples
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Primary user signal detection techniques
• The received signal will have more energy when the channel is
busy than when it is idle.
• False alarms (Type I errors) occur if an idle channel is detected
as busy and missed detections occur when a busy channel is
detected as idle.
• A missed detection (Type II error) could potentially lead to a
collision with primary user leading to wasted transmissions for
both primary user and secondary user.
• The performance of a detector is characterized by two
parameters, probability of missed detection(PMD) and
probability of false alarm(PFA) using classical formulation of
binary Neyman-Pearson test, defined as,
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Primary user signal detection techniques
• A typical receiver operating characteristic (ROC)
• Choosing different sensors, detection algorithms or sensing
parameters leads to different ROCs
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Energy Detector
• The signal is passed through bandpass filter in order to limit the noise of
bandwidth,W and is integrated over time interval.
• The output from integrator is compared to a predefined threshold.
• The comparison is used to discover the absence of the primary user.
• Threshold value can be fixed or variable based on the channel conditions.
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Energy Detector
• Here w(k) and s(k) are zero mean complex Gaussian random
variables with variances
• Let
denote vector of n observed
samples.
• We denote standard deviation
• Neyman – Pearson is a threshold detector on log-likelihood
ratio(LLR)
• Where is a suitably chosen threshold
• The detector is equivalent to H1 if
• Statistic z is a scaled version of a standard
with 2n degrees of freedom.
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random variable,
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Energy Detector
• If
are independent real Gaussian variables with zero means
and unit variances.
• Probability density function is given by,
• For probability
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Energy Detector
• Hence PFA and PMD for the energy detector can be obtained as,
• One disadvantage of energy detector is that at low SNR, the
number of samples required to achieve specified performance
metrics is proportional to
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Energy Detector
• Non-zero mean case:
• In 802.22 standard, the primary signal contains known
synchronization sequence.
• These sequence are repeated to facilitate detection.
• The known sync sequence are used in matched filter
• Under H1, Statistic z is a non-central
distributed with 2n
degrees of freedom.
• Non-centrality parameter,
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Energy Detector
• Probability of distribution,
•
•
•
•
•
•
is the generalized Marcum Q function.
In - is the modified Bessel function of order n.
Energy detection Under fading:
Consider a block fading environment,
Under H1, Received signal is expressed as
h- random variable, assumed fixed over n samples, represents
fading.
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Energy Detector
• PFA remains unchanged, since there is no fading under H0
• Under H1, average probability of detection is computed from,
• Where , is the pdf of SNR
• In case of weak signal detection, it is performed by higher
order detectors.
• Another weakness is at low SNR, the number of samples
increases as
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Cyclo stationary Feature Detector
• Often PU(primary user) signal structure is known.
• Such as data rates, modulation type, carrier frequency, location
of guard bands are known.
• Digitally modulated signals have periodic features.
• The carrier frequency and symbol rate can be estimated via
square law devices.
• Block diagram of Cyclo-stationary feature detector block
diagram
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Cyclo stationary Feature Detector
• Cyclo-stationary feature detector exploits the periodicity in the
received primary user signal to identify the presence or
absence of the primary licensed users in the frequency band
spectrum.
• Primary user network uses pilot tone frequency
• The use of cyclic prefix leads to periodic signal structures.
• The mean and correlation sequences of such signals exhibit
periodicity and hence called Cyclo-stationary signals.
• The test statistic of a cyclic detector,
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Cyclo stationary Feature Detector
• The received signal y(n) is written as
• Where
are mutually independent zero-mean wide
stationary processes, then for large N,
• Where
• This detector is implemented via Fast Fourier transform
• Knowledge of noise variance is not required to set the
detection threshold.
• The performance of the detector degrades in the presence of
timing and frequency jitters and RF nonlinearities.
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Matched Filter
• Often the pilot or sync sequences used in the primary network
are known to secondary users.
• Let s(n) denote the pilot sequence, n=1,2,3….N
• Assuming perfect synchronization, the received signal at the
secondary user is
• Where w(n) is additive white Gaussian noise and h- represents
unknown channel gain.
• The optimal detector is the matched filter.
• Matched filter detection is performed by projecting the
received signal in the direction of the pilot, s(n)
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Matched Filter
• The test statistic is
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Matched Filter
• The performance of the detector is given by
• SNR is defined by
• At low SNR, the number of required samples is of the order of
in contrast with
samples required by the energy
detector
• This is a significant advantage.
• The performance is degraded in the presence of frequency and
timing offsets as well as fading and delay spread.
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Cooperative sensing
• Earlier we noted that the performance of a single detector can
be severely degraded due to fading, shadowing or faulty
sensor.
• This is a motivation for cooperative sensing, where
observations from multiple secondary users are combined to
improve detector performance.
• Received signal at the kth secondary user is given by,
• Noise sequence are assumed to be independent and identically
distributed in time n and mutually independent across the
sensors.
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Cooperative sensing
• Channel gain coefficient,
is assumed to be independent
across the sensors.
• Considerable overhead is required to transmit all
• Performs local detection and passes only binary decision
variables to a fusion center (FC).
• LLR is quantized and sent to FC.
• Depending on the level of complexity, FC has many choices
for its fusion rule,
• The cooperative scheme requires a control channel and a
trusted spectrum broker.
• Latency is an important issue.
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Cooperative sensing
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Cooperative sensing
• The time required to sense the channel, report the
measurements to FC and for the FC to detect white space and
allocate spectrum to the user must be considerably less than
the channel free time.
• Let
denote the local performance indices of the
kth sensor
• If the sensor observations and thus local decisions are
conditionally independent, then
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Cooperative sensing
• FC has access to the individual sensor statistics and it can
combine in many classical ways
• Equal gain combining(EGC)
• Selection combining
• Consider the case where the K sensor statistics are
independent.
• Effective SNR is the sum of the individual SNR and adding K
non-central
random variables each with 2N degrees of
freedom and has a non-centrality parameter
• Cooperative sensing can be used to localize the active
transmitters.
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Other Approaches
• The frequency nonselective flat fading assumes a narrowband
channel model.
• When the spectrum to be sensed is wideband, there are
multiple challenges.
• First, one may consider partially overlapping sub-channels for
each of flat fading channels.
• If the primary traffic is heavy, secondary user(SU) would seek
to monitor multiple bands which entails increased sampling
rates, receiver complexity and energy consumption.
• Multi-resolution and wavelet based methods are proposed
for wideband problem
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Other Approaches
• Power spectral density is smooth within each sub-channel but
discontinuous across sub-channel boundaries.
• By using wavelet transform, the discontinuous can be
identified and thus spectrum activity is detected.
• Sub-Nyquist sampling schemes, in conjunction with wavelet
based edge detection are used to provide coarse estimates of
spectrum occupancy and transmitter location.
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Fundamental Trade-offs in spectrum sensing
• Performance versus constraint:
• Fundamental question in designing the spectrum opportunity
detector is how to choose the detector operating point
• It achieves optimal tradeoff between false alarms and missed
detection.
• Such trade-off should be addressed in terms of MAC layer
performance:
• The throughput of the secondary user.
• Probability of colliding with primary users.
• At MAC Layer:
• Performance is measured by the throughput of the secondary
user and the interference to the primary users.
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Fundamental Trade-offs in spectrum sensing
• The objective is to maximize the throughput under a constraint
on the maximum outage probability , that the interference at
an active primary receiver exceeds the noise floor , such
events are referred to as Collisions with primary users.
• The figure of merit at the MAC layer are given by probability,
PS of successful data transmission and probability PC of
colliding with primary users.
• The objective and constraint at the MAC layer is given by,
• For a complete data transmission, acknowledgement signal is
required.
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Fundamental Trade-offs in spectrum sensing
• For successful data transmission, following three events occur
in sequence:
• Thus we have
• Where
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Fundamental Trade-offs in spectrum sensing
• For best effort, delivery applications, acknowledgement are
not required to confirm the completion of data transmission.
• In this case
• Probability of collision
• Clearly,
• Global Interference Model:
• Consider this model, where the transmission from every
primary user of interest affects the reception at B and the
transmission from A affects the reception at every primary user
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Fundamental Trade-offs in spectrum sensing
• PHY-MAC translation under the global interference
model:
• Successful transmission from A and B results only from
opportunities
• Every correctly identified opportunities leads to successful
transmission.
• Every missed detection results in collision with primary users.
• These properties leads to simple relationship between
• {PFA and PMD} and {PS, PC}
• PS = (1- PFA) Pr[H0], PC = PMD
• To maximize PS under the constraint of
, we obtain
optimal operating point
for the spectrum sensor.
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Fundamental Trade-offs in spectrum sensing
• Local Interference model:
• The relationship between PHY and MAC has complex
dependency on the applications.
• PHY-MAC translation under the Local Interference Model
• Used in applications, PC ≠ PMD
• For applications with guaranteed delivery, correctly detected
opportunities may lead to failed data transmission and miss
detections may lead to successful data transmission.
• For best-effort delivery, correctly detected opportunities
always result in successful data transmission and miss
detections may also lead to successful data transmission
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Fundamental Trade-offs in spectrum sensing
• Impact of MAC handshaking:
• The fundamental deficiency of detecting spectrum
opportunities from detecting primary signals resembles the
hidden and exposed terminal problem in conventional ad hoc
networks of peer users.
• It is necessary to consider the use of RTS(Request to
send)/CTS(Clear to send)handshaking to enhance the detection
performance.
• Although RTS/CTS signaling can improve the performance of
opportunity detection at the physical layer, it leads to
decreased throughput at the MAC layer for best-effort delivery
applications.
• For enhanced LBT, transmitter A first detects a chosen set of
primary transmitters.
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Fundamental Trade-offs in spectrum sensing
• If there are no signals from this set, it transmits an RTS to B.
• Upon receiving the RTS(automatically indicates the absence of
interfering primary transmitters).
• A successful exchange, A starts to transmit data to B.
• PHY/MAC translation with RTS/CTS signaling
• Correctly detected opportunities always result in successful
data transmission as well as missed detections.
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Fundamental Trade-offs in spectrum sensing
• Sensing Accuracy versus Sensing overhead:
• Increasing the sensing time improves the fidelity of the sensing
outcomes thus reducing the overlooked spectrum
opportunities.
• Increasing the sensing time results in less transmission time.
• Tradeoff between sensing accuracy and sensing overhead
depends on the SNR level, duration of spectrum opportunities
and the interference constraint,
• Let N represents the slot length, n denotes the duration of
sensing window.
• PFA(n) and PMD(N) denote the performance metrics based on
sensing window of length, n
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Fundamental Trade-offs in spectrum sensing
• Assuming the channel is free, fractional time that the channel
will be accessed is 1-PFA(n) and fractional slot available for
transmission is (N-n)/N
• Spectral efficiency metric is defined as,
•
•
•
•
•
For a specified PD(Interference constraint), PFA is given by,
Which decreases monotonically with n.
Should select the value of n that maximizes
As SNR increases, the required sensing window length
decreases and efficiency increases.
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Spectrum Sharing Models of DSA
• Spectrum sharing is the simultaneous usage of a specific radio
frequency band in a specific geographical area by a number of
independent entities through mechanisms other than traditional
multiple and random access techniques.
• Key factors of spectrum sharing:
• First, independent assumption of coexisting system means
legacy MAC mechanisms, which are used to share resources
among users in a cellular system
• Second, emphasis is on scenario where mechanisms to
facilitate spectrum sharing exists.
• Dynamic spectrum sharing has broad suggestion that
encompass various approaches to spectrum reform.
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Spectrum Sharing Models of DSA
• Dynamic spectrum access strategies
categorized under three models.
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be
broadly
46
Dynamic exclusive use model
• This model maintains the basic structure of the current
spectrum regulation policy.
• Spectrum bands are licensed to services for exclusive use.
• The main objective is to introduce flexibility and improve
spectrum efficiency.
• Two approaches have been proposed for this model:
• Spectrum property rights
• Dynamic spectrum allocation
• Spectrum property rights:
• It allows licensees to sell and trade spectrum and to freely
choose technology
• Economy and market play an important role in driving towards
the most profitable use of limited resource.
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Dynamic exclusive use model
• Dynamic spectrum allocation:
• The second approach was brought by European DRIVE
project.
• It aims at improving spectrum efficiency through dynamic
spectrum assignment by exploiting spatial and temporal traffic
statistics of different services.
• In a given region and at a given time, spectrum is allocated to
services for exclusive use.
• This allocation varies at a faster scale.
• These approaches cannot eliminate white space in the
spectrum.
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Opening Sharing model
• Also referred to as spectrum commons.
• Employs open sharing among peer users as the basis for
managing a spectral region.
• Wireless services operating in the unlicensed industrial,
scientific and medical(ISM) radio band (eg. Wi-Fi) have been
analyzed.
• Centralized and distributed spectrum sharing strategies have
been initially investigated to address technological challenges
under this spectrum management model.
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Hierarchical Access model
• This model has hierarchical access structure with primary and
secondary users.
• The basic idea is to open licensed spectrum to secondary users
while limiting the interference perceived by primary
users(licensees).
• Three approaches to spectrum sharing have been considered:
• Spectrum underlay
• Spectrum overlay
• Spectrum interweave
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Spectrum Underlay
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Underlay Spectrum Sharing
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Spectrum Underlay
• Underlay approach imposes severe constraints on the
transmission power of secondary users so that they operate
below the noise floor of primary users.
• By spreading transmitted signals over a wide frequency
band(UWB) secondary users can achieve a short range high
data rate with extremely low transmission power.
• To avoid any interference to the primary users, the underlay
system can use interference techniques such as notching and
waveform adaptation.
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Spectrum Overlay
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Overlay Spectrum Sharing
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Spectrum Overlay
• Spectrum overlay was first envisioned by Mitola under the
term spectrum pooling and then investigated by
DARPA(Defence Advanced Research project academy) next
generation programme under the term opportunistic spectrum
access.
• This approach does not impose severe restrictions on the
transmission power of secondary users, but rather on when and
where they may transmit.
• It directly targets at spatial and temporal spectrum white space
by allowing secondary users to identify and exploit local and
instantaneous spectrum availability.
• Compared to dynamic exclusive use and open sharing models,
this hierarchical model is perhaps the most compatible with
current spectrum management policies and wireless systems.
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Spectrum Overlay
• This results in two main design goals,
• Minimum interference to licensed transmissions
• Maximum exploitation of the gaps in the time-frequency
domain.
Spectrum Interweave:
• The interweave approach is based on opportunistic
communication.
• There exists temporary frequency voids in a frequency band,
which are referred to as spectrum holes not used by the
licensed/primary users.
• Spectrum holes pop up according to changes in time and
geographical locations.
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• CR must constantly monitor the spectrum typically via
physical layer spectrum sensing and to adopt certain medium
access strategies to use spectrum holes as transmission
opportunities for secondary transmissions with minimum
interference to users/nodes
Opportunistic Spectrum Access: Basic components
• The term Opportunistic Spectrum Access(OSA) will be
adopted throughout.
• Basic components of OSA include spectrum opportunity
identification, spectrum opportunity exploitation and
regulatory policy.
• The opportunity identification module is responsible for
accurately identifying and intelligently tracking idle frequency
bands that are dynamic in both time and space.
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Opportunistic Spectrum Access: Basic components
• The opportunity exploitation module takes input from the
opportunity identification module and decides whether and
how a transmission should take place.
• The regulatory policy define basic etiquette for secondary
users to ensure compatibility with legacy systems.
• The design objective of OSA is to provide sufficient benefit to
secondary users while protecting spectrum licensees from
interference.
• The tension between the secondary users desire for
performance and the primary users need for protection dictates
the interaction across opportunity identification, opportunity
exploitation and regulatory policy.
• Thus OSA calls for cross layer approach that integrates signal
processing and networking.
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Unlicensed and Licensed Spectrum Sharing
• Components of CRN(cognitive Radio Network):
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Unlicensed and Licensed Spectrum Sharing
• Components of CRN:
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Unlicensed and Licensed Spectrum Sharing
• Basic elements of the primary and CR network:
• Primary Network:
• An existing network infrastructure is generally referred to as
primary network.
• It has exclusive right to a certain spectrum band.
• Eg. Common cellular and TV broadcast network
•
•
•
•
Primary User or licensed user:
Has the license to operate in a certain spectrum band.
This access can only be controlled by primary base station.
Primary users do not need any modifications or additional
functions for coexistence with CR base stations and CR users.
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Unlicensed and Licensed Spectrum Sharing
• Primary Base station or licensed Base station:
• It is a fixed infrastructure network component which has a
spectrum license such as base-station transceiver system(BTS)
in a cellular system.
• Primary base-station does not have any CR capability for
sharing spectrum with CR users.
• CR Network:
• CR network, Dynamic spectrum access network, secondary
network and unlicensed network does not have license to
operate in a desired band.
• The spectrum access is allowed only in an opportunistic
manner.
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CR Network:
• Components of an CR network:
• CR users:
• CR user(unlicensed user, CR user, secondary user) has no
spectrum license.
• Additional functionalities are required to share the licensed
spectrum band.
• CR base station:
• CR base-station (unlicensed base-station, CR base-station,
secondary base-station) is a fixed infrastructure component
with CR capabilities
• It provides single hop connection to CR users without
spectrum access license
• CR users can access other networks.
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CR Network:
• Spectrum Broker or scheduling server:
• It is a central network entity that plays a role in sharing the
spectrum resources among different CR networks.
• It can be connected to each network and can serve as a
spectrum information manager to enable coexistence of
multiple CR networks.
• CR networks are operated under the mixed spectrum
environment that consists of both licensed and unlicensed
bands.
• CR users can either communicate with multi-hop manner or
access the base-station
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CR Network:
• In CR networks, there are three different access types :
• CR Network Access:
• CR users can access their own CR base-station both on
licensed and unlicensed spectrum bands.
• CR Ad-Hoc Access:
• CR access can communicate with other CR users through
ad-hoc connection on both licensed and unlicensed spectrum
bands.
• Primary Network Access:
• CR users can also access the primary base-station through
licensed band.
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Unlicensed Spectrum Sharing
• Unlicensed frequency bands are portion of spectrum used by
devices that operate in a way that is not constricted by licenses
and are prone to interference.
• Commonly used unlicensed bands are 2.4GHz ISM(Industrial,
scientific and medical) band used by IEEE 802.11 and
Bluetooth devices and 5GHz UNII(Unlicensed National
Information Infrastructure) band used by IEEE 802.11a and
European HyperLAN standards.
• Open spectrum policy caused an impressive variety of
technologies and innovative uses.
• The capacity of open spectrum access and the quality of
service they can offer depends on the degree to which a radio
can be designed to allocate the spectrum efficiently.
• CR networks can be designed for operation on unlicensed
bands such that the efficiency
is improved.
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Unlicensed Spectrum Sharing
• CR network on unlicensed band architecture.
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Unlicensed Spectrum Sharing
• Since there are no license holders, all network entities have the
same right to access the spectrum bands.
• Multiple CR networks coexists in the same area and
communicate using the same portion of the spectrum.
• Intelligent spectrum sharing algorithms can improve the
efficiency of spectrum usage and support high QoS.
• In this architecture, CR users focus on detecting the
transmissions of other CR users.
• Unlike the licensed band operations, the spectrum handoff is
not triggered by the appearance of other primary users.
• All CR users have the same right to access to access the
spectrum.
• Sophisticated spectrum sharing methods among CR users are
required.
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Unlicensed Spectrum Sharing
• First, it is easy to develop innovative technologies to operate in
unlicensed bands since the approval process is similar to
licensed technologies.
• Second, there is no cost to the customer of using such bands.
• Certain transmission power caps are used described by ITU-R
5.138, 5.150
• These may result in localization of interference by which
numerous unlicensed devices operating in those bands are
spatially/temporally distributed.
• Successful deployment and rapid growth of WLAN
technologies such as IEEE 802.11 is one of the creation of
unlicensed bands.
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Licensed Spectrum Sharing
• Licensed bands are frequency bands assigned exclusively to a
licensee, for instance a specific mobile operator.
• Such license also stipulates a specific technology to be used in
a band . Eg. GSM(Global system for Mobile communication)
or UMTS(Universal Mobile Telecommunications System)
• Regulators such as Ofcom in United Kingdom have shown
interest in assigning spectrum bands as “technology neutral”.
• Depending on the technology used in the licensed band of a
specific service provider, various MAC techniques are used to
allow end users to share the medium.
• Sometimes there exists temporally unused spectrum holes in
the licensed spectrum.
• CR networks can be deployed to exploit these spectrum holes
through cognitive communication techniques.
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Licensed Spectrum Sharing
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Licensed Spectrum Sharing
• CR network coexists with primary network at the same
location and on the same spectrum band.
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Licensed Spectrum Sharing
• There are various challenges for CR networks on licensed
band due to the existence of the primary users.
• The main purpose of CR network is to determine the best
available spectrum and at the detection of the presence of
primary users.
• The channel capacity of the spectrum holes depends on the
interference at the nearby primary users.
• The interference avoidance with primary users is the important
issue in this architecture.
• If primary users appear in the spectrum band occupied by CR
users, CR users should vacate the current spectrum band and
move to the new available spectrum immediately called
spectrum handoff.
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Licensed Spectrum Sharing
• If the cellular system uses a frequency reuse plan, it is not
possible to share channels a, b or c with the broadcasting
system due to interference.
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Licensed Spectrum Sharing
• In case of spectrum sharing with similar coverage area,
misplacement of the broadcast signals of the two systems can
have negative impact in terms of Co-Channel Interference.
• This is because resource management is not properly
coordinated.
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Fundamental Limits of Cognitive Radio
• Mitola envisioned a radio that could make decisions as to the
network, modulation and coding parameters based on its
surroundings called a “smart radio” a cognitive radio.
• Such radios could make decisions by their current location and
spectral conditions.
• It removes unnecessary regulatory barriers to new secondary
market-oriented policies such as:
• Spectrum Leasing: Allowing unlicensed users to lease any
part or all of the spectrum of a licensed user.
• Dynamic Spectrum leasing: Temporary and opportunistic
usage of spectrum rather than a longer term sublease.
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Fundamental Limits of Cognitive Radio
• Private Commons:
• A licensee could allow unlicensed users access to the spectrum
without contract, optionally with an access fee.
• Interruptible Spectrum leasing:
• Suitable for a leaser that wants a high level of assurance that
any spectrum temporarily in use or leased to an incumbent
cognitive radio could be efficiently reclaimed if needed.
• Cognitive radios have the ability to listen to the surrounding
wireless channel, make decisions on the fly and encode using a
variety of schemes.
• In order to exploit these abilities fully , consider an example.
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Fundamental Limits of Cognitive Radio
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Fundamental Limits of Cognitive Radio
• As shown in the left, suppose sender X1 is transmitting over
the wireless channel to receiver Y1 and a second incumbent
user, X2 wishes to transmit to a second receiver, Y2
• In the current secondary spectrum licensing, incumbent user
X2, a cognitive radio that is able to sense the presence of other
transmitting users, would either wait until X1 has finished
transmitting before proceeding or possibly transmit over a
different frequency band.
• Rather than forcing X2 to wait, it allows X2 to transmit
simultaneously with user X1 at the same time in the same band
of frequencies.
• The wireless nature of the channel will make interference
between simultaneously transmitting users unavoidable.
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Fundamental Limits of Cognitive Radio
• My making use of the capabilities of cognitive radio, it is able
to potentially mitigate the interference.
• Cognitive radio channel is defined as a two sender(X1, X2),
two receiver (Y1, Y2) interference channel in which the
cognitive radio transmitter X2 is non-causally given by a genie
the message X1 plans to transmit. X2 can either mitigate the
interference , help X1 in transmitting its message or smooth
mixture of both.
• A simple cognitive radio channel is considered as a two
transmitter, two receiver information theoretic interference
channel in which sender 2(cognitive radio) obtains, or is given
by a genie, the message sender 1 plans to transmit.
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Fundamental Limits of Cognitive Radio
• Cognitive radio may then simultaneously transmit over the same channel as
opposed to waiting for an idle channel as in a traditional cognitive radio
channel protocol.
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Fundamental Limits of Cognitive Radio
• An intuitively achievable region for the rates (R1 and R2) at
which X1 can transmit to Y1, and X2 to Y2, simultaneously
been constructed.
• When X2 has a prior knowledge of what X1 will transmit or
interference it will encounter, we have two possible course of
action.
• a) It can try and mitigate the interference. X2 is layering on its
own independent information to be transmitted to Y2. This
strategy yields points of higher R2 and lower R1 in the
cognitive channel region.
• b) It selflessly act as a relay to reinforce the signal of user X1.
That is, since X2 violate on X1 spectrum, only X1 should
somehow benefit. This strategy yields points of high R1 and
low R2 in the cognitive channel region.
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Fundamental Limits of Cognitive Radio
• From the figure, region(1) is the time sharing region of two
independent senders.
• Region(2) is the best known achievable region for the
interference channel.
• Region(3) is the achievable region described for the cognitive
radio channel.
• Region(4) is an outer bound on the cognitive radio channel
capacity.
• The resulting achievable region in the presence of additive
white Gaussian noise is plotted as the cognitive channel
region.
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Fundamental Limits of Cognitive Radio
• Here we can see four regions:
• The time sharing region (1) displays the result of pure time
sharing of the wireless channel between users X1 and X2.
Points in this region are obtained by letting X1 transmit for a
fraction of the time, during which X2 refrains.
• The interference channel region(2) corresponds to the best
known achievable region of the classical information theoretic
interference channel. Both the senders encode independently
and there is no message knowledge by either transmitter.
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Fundamental Limits of Cognitive Radio
• The cognitive channel region (3) is an achievable region. In
this case X2 received the message of X1 non-causally from a
genie and X2 uses a coding scheme that combines interference
mitigation with relaying the message of X1. Both users get
benefit from this scheme.
• The modified MIMO bound region(4) is an outer bound on the
capacity of this channel. The 2x2 MIMO Gaussian broadcast
channel capacity region, where we have restricted the transmit
covariance matrix to more closely resemble our constraints,
intersected with the capacity bound on R2 for the channel in
the absence of interference from X1.
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EC 8071- COGNITIVE RADIO
UNIT IV MAC AND NETWORK LAYER DESIGN
FOR COGNITIVE RADIO
MAC for cognitive radios – Polling, ALOHA, slotted
ALOHA, CSMA, CSMA / CA, Network layer design –
routing in cognitive radios, flow control and error
control techniques.
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INTRODUCTION-MULTIPLE ACCESS
• The simplest way of interconnecting two computer host is
using a point to point link with a host on each end.
• As the number of hosts increases, this approach may be
inadequate, since large number of links are needed.
• In this case we use broadcast network, where all hosts share a
common transmission media.
• To share a common media (eg. Cable or wireless channel)
efficiently, all hosts must follow a set of rules to access the
media.
• Host should be able to check the availability of the media and
resolve collisions.
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INTRODUCTION-MULTIPLE ACCESS
• Since the bandwidth of the media is limited, it is desirable to
share it efficiently.
• The sharing rules are defined as media access control
protocols which are implemented in the link layer.
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MAC for Cognitive Radios
• Consider a set of frequency bands,
• M = { 1,2, …, M}
• At time tn, Cognitive radio network operation allows an update
of spectrum utilization.
• The nth observation or allocation time interval is
• Due to the opportunistic nature of each link modelled as
morkov chain, the ith frequency band is available with the
probability and is invariant to time.
• We define indicator function:
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MAC for Cognitive Radios
• The probability mass function of Bernoulli random variable at
the ith frequency band is
• Where,
• For reliable CR operation, spectrum sensing is necessary, so
that CR – Tx can have information about the availability of
each frequency band.
• For network operations, the strategy would be related to
• Case 1: is known , Case 2: is unknown
• Case 3:
can be detected or estimated via some CRN sensing
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MAC for Cognitive Radios
• Traditional CR functions:
• When
is known, the spectrum sensing strategy for a CR is
simply to select the channel.
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Multi-channel MAC (McMAC)
•
•
•
•
Allows a number of nodes in the same neighborhood to transmit
concurrently on different channels without interfering with one another.
Carrier sensing can be coupled with an efficient channel selection
mechanism to pick the clearest channel for transmission.
A wireless device or node can only use one radio due to its half duplex
nature.
With the development of SDR and CR, multi-channel MAC plays a critical
role in CRN MAC.
Advantages of multi-channel MAC protocols in CRN:
• Reduce collisions
• Enable more concurrent transmissions
• Better bandwidth usage even with the same aggregate capacity.
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Multi-channel MAC (McMAC)
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Multi-channel MAC (McMAC)
• Cognitive multi-channel MAC appears as an enhanced version
of McMAC
• Every protocol operating frame is divided into three phases:
• Spectrum sensing phase
• CSMA(carrier sense Multiple Access) contention phase
• Data transmission phase
• In cognitive multichannel MAC, devices are able to sense the
outer environment , information of the network and the
information of other interference resources(i.e primary
systems).
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Multiple Access Scheme
• In wireless network, mobile users share the wireless channel.
• An important network control function is medium access
control(i.e) how to share the channel among the users
efficiently. eg. With high spectrum efficiency i.e, users should
have equal chance of accessing the channel.
• In classification of multiple access schemes, Channelization is
widely adopted in traditional wireless networks
• In this, the wireless channel is partitioned into a number of subchannels, eg. Frequency band, time slot, or spreading code
• Then sub-channels are assigned to users.
• The processing of sub-channels are done using several access
schemes.
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Classification of MAC schemes
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Polling
• A polling system is a special type of queuing system with one
server and m stations (users), where one station is served at a
time.
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Polling
• Each station is represented by a queue temporarily storing
arriving customers, while customers at each station follows a
random process.
• Each customer requests service from the server and departs the
system when its service is completed.
• Stations in a polling system may have,
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Polling
• When the server finishes serving a station, it may decide which
station to serve next by following a fixed order (eg.,cyclic) or a
random selection.
• The time between the end of a service and the beginning of the
next service is the switchover time.
• There are three kinds of service policies in a polling system:
• Limited service polling system:
• The server continuously serves a station until either the station
is empty or a predetermined number of customers have been
served.
• Special case-limited system where atmost one customer is
served whenever the server visits a station.
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Polling
Gated service system:
• The server continuously serves a station until either the station
is empty or all customers that arrived before the station is
polled are served.
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Polling
Exhaustive service system:
• The server serves all customers in a station continuously and
leaves the station only when the station buffer is empty.
• The fig. shows a gated service system with m=1, where the
blocks with different shades represent the service times of
customers.
• Assume arrival process is poisson with rate λ
• Let X denote the service time of a customer with mean
• System utilization
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Polling
• Let
denote the duration of jth switchover time
• Consider ith customer arriving at the system
• Before it is served, it has to wait in the queue until all
customers that arrived earlier are served.
• The waiting time in the queue includes the residual time, Ri,
the service time and the next switchover time
• The expected waiting time for customer ‘i’ is given by,
• Expected waiting time in queue for the single user gated
service system as ,
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ALOHA
• Original ALOHA also called pure ALOHA was the earliest
MAC scheme developed for packet radio networks.
• With pure ALOHA, a station transmit a packet whenever it
wants to.
• After the transmission, the station listens for an amount of time.
• If no acknowledgment is received by the sender, which
indicates a collision, the frame is sent again.
• Pure ALOHA is a very simple multiple access protocol, but its
throughput is low.
• Assume all packets have same length and the transmission time
of a packet is τ seconds.
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ALOHA
• After a round-trip time R > τ , a collision may be detected.
• After a random scheduling delay D, a retransmission is tried, T
• All transmitted packets including retransmissions follow a
poisson process with rate g packets/s.
• If a node sends a packet at time t and there is no other
transmission during [t- τ, t+ τ], the packet will be successfully
received.
• Since arrival process is poisson, the number of transmissions
has a poisson distribution with parameter 2gτ.
• Therefore, successful rate is given as
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ALOHA
• Let G = gτ be the normalized load, the throughput of pure
ALOHA is
• By differentiating S with respect to G and setting the derivative
to zero. The maximum throughput of pure ALOHA is given as,
• 2τ can be interpreted as vulnerable time of pure ALOHA
• If on average there is only one packet transmission during
vulnerable time, pure ALOHA achieves its maximum
throughput.
• Otherwise channel is either under loaded(channel bandwidth
not fully exploited) or overloaded (waste of channel bandwidth
due to collision)
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SLOTTED ALOHA
• It is an extension of pure ALOHA for improving throughput.
• The time is divided into slots and the length of one time slot is
equal to the packet transmission time τ.
• Assume all nodes are synchronized
• When a node has a packet to send, it starts to send it at the
beginning of the next time slot.
• If no other transmissions are in the same time slot, the
transmission succeeds.
• Otherwise, a collision occurs and the packet will be
retransmitted after a random delay.
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SLOTTED ALOHA
• Similar to pure ALOHA analysis, assume that all transmitted
packets, including retransmission form a poisson stream with
rate g packets/s
• If a node sends a packet in time slot t and there is no other
transmission during the same time slot, the packet will be
successfully received.
• Assume the number of packets transmitted in a time slot has a
poisson distribution with parameter G = g τ
• Throughput of the slotted aloha is
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SLOTTED ALOHA
• Differentiating S with respect to G and setting the derivative to
zero, maximum throughput of the slotted aloha is
• By adopting a time-slot system(with synchronization), the
throughput is doubled.
• The length of one time slot (τ ) can be interpreted as vulnerable
time (when G=1), slotted aloha achieves maximum throughput.
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CSMA
• One major cause of ALOHA‘s low throughput problem is that
users do not try to avoid collisions.
• Users start transmission whenever they want even when the
channel is busy and collision is predestined.
• Efficient enhancement is to let each user sense the medium
before starting transmission.
• If the channel is sensed busy, the user holds its packets until the
medium is free. Such an idea is incorporated in carrier sense
multiple access(CSMA)
• Following three strategies can be used to sense the channel:
• I-persistent CSMA
• Non-Persistent CSMA
• P-Persistent CSMA
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I-persistent CSMA
• A user with data frame waiting senses the medium and sends
the frame immediately if the medium is free.
• If the medium is busy, the user continuously senses the medium
and starts transmitting as soon as it finds the medium free,
avoids the idle stage of medium.
• When more than one user is sensing the medium(when it is
busy), they will start transmitting simultaneously when the
channel becomes idle, leading to collision.
• NON-Persistent CSMA:
• The node waits a random amount of time before sensing again,
when it senses a busy medium.
• Backlogged users will come back to sense the medium at a
different time and the collision rate can be reduced.
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P-persistent CSMA
•
•
•
•
•
•
Demerits:
The medium utilization is reduced
Increases waiting time for backlogged users.
P-Persistent CSMA:
It is used when time is divided into slots.
A backlogged user senses the medium and starts the
transmission with probability P if the medium is found free.
• Otherwise it senses the medium at the beginning of the next
time slot.
• P-Persistent CSMA combines the advantages of the preceding
two schemes and can reduce the collision rate and improves the
medium efficiency.
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P-persistent CSMA
• Although CSMA can effectively reduce the chance of collision,
it cannot completely eliminate collision.
• An example of collision in CSMA
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P-persistent CSMA
• At T0, Node A senses an idle channel and starts transmitting.
• It takes ‘a’ seconds (propagation delay) for the first bit of the
packet to reach node B.
• Before T0 + a, Node B still senses an idle channel.
• If Node B starts transmitting during (T0, T0 + a ), there is a
collision.
• Therefore the vulnerable time of CSMA is the maximum
propagation delay ‘a’.
• For unslotted non-persistent CSMA, assume that there are an
infinite number of stations and fixed length packets with
transmission time τ seconds.
• Also assume that the propagation delay for all other stations to
hear a packet is ‘a’ seconds.
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P-persistent CSMA
• Then ‘a’ seconds after a packet is transmitted, all other stations
know that the channel is busy and do not attempt to access the
channel.
• Assume that arriving packets follow poisson distribution with
the rate s packets/s.
• When a transmission attempt fails, the packet is rescheduled for
retransmission.
• To simplify the analysis, we assume the overall packets to be
transmitted follow a poisson process with rate g seconds/s,
including both arriving packet and rescheduled packets.
• To have successful transmission, the channel has to be idle
when the sender starts sensing, and there is no other
transmissions within the next ‘a’ seconds period.
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P-persistent CSMA
• Therefore we have
•
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P-persistent CSMA
• Each cycle starts with the period of idle time
and then
transmission occurs when a station with packets waiting finds
the channel idle.
• Interfering transmissions are possible during the a seconds after
the first transmission starts.(i.e vulnerable time of CSMA)
• Z is defined as the time between the beginning of the first
transmission and that of the last interfering transmission.
• After the end of the last interfering transmission, another a
seconds pass before a new cycle starts, so that all stations are
aware that the current transmission is over.
• The length of the cycle:
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P-persistent CSMA
• From Renewal Theory,
•
Pr{channel idle} =
• Since the arrival process is poisson with exponentially
distributed interarrival times, we have
• The probability of the channel being idle is therefore,
• Successful packet rate is
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P-persistent CSMA
• Normalized rates are
• We have
• CSMA/CA
• Collision is more costly in wireless networks than in wire line
networks.
• When a collision occurs, bandwidth which is limited in many
wireless networks is wasted and energy is also wasted on failed
transmissions.
• In wired networks, the carrier sense multiple access with
collision detection(CSMA/CD) can be adopted to detect
collision and the sender can immediately stop the transmission,
thus avoid wasting bandwidth and energy on transmitting the
remaining part of the corrupted frames.
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•
CSMA/CA
• In CSMA with collision avoidance(CSMA/CA),two
mechanisms are incorporated to avoid collision:
• A set of delays , termed Interframe Spaces(IFS), that amounts
to a priority system
• A contention window and binary exponential back-off
• When a station has a frame to transmit, it first senses the
channel.
• Channel is found to be idle, it waits for an interval of IFS to see
if the channel is still idle. If so, the station starts transmission.
• Channel is found busy, the backlogged station defers its
transmission and keeps on sensing the channel until the current
transmission is over. Then the station waits for IFS
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CSMA/CA
• If the channel is still idle, the station backs off further for a
random period of time. It starts transmitting only when the
channel remains idle after the random back-off time.
• The back-off timer is decreased only when the channel is idle.
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CSMA/CA
• The hidden terminal and exposed terminal problems in wireless
LANs are inherent from the use of wireless transmissions.
• Consider a scenario shown,
• Nodes B and C are outside of each other’s transmission range
and Node A is inbetween and can hear both Nodes B and C’s
transmissions
• So, Node B and C are hidden from each other with respect to
Node A.
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CSMA/CA
• If CSMA/CA is used, Node C senses an idle channel because it
cannot hear Node B’s ongoing transmission.
• Node C therefore starts transmitting data to node A and a
collision occurs at Node A. This is called hidden terminal
problem.
• Now consider a different scenario,
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CSMA/CA
• There are four nodes in the system,
• If CSMA/CA is used, Node C detects a busy channel and waits
until Node A’s transmission is over, resulting in a waste of
bandwidth.
• The hidden terminal problem can be solved by incorporating a
request-to-send (RTS) and clear-to-send(CTS) handshake
before data frame transmission.
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CSMA/CA
• When a node has a frame to send, it first broadcasts an RTS
message carrying the time needed to transmit the frame.
• The target node, if it is free, responds with CTS broadcast.
• All other nodes that hear RTS or the CTS, mark the channel as
busy for the duration of the requested transmission.
• Thus collision due to hidden terminals can be avoided.
• CSMA/CA is adopted in IEEE802.11 MAC , which has
become the most popular protocol for single or multihop
wireless networks.
• The achievable maximum throughput(MT) under the best
scenario when,
• Channel is error free
• Exactly one station is active during any transmission cycle
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CSMA/CA
• Where
is the minimum back-off window size
•
- is the slot time
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CSMA/CA
• The data transmission delay and the ACK transmission delay is
given by, IEEE 802.
• The control overhead of IEEE 802.11 MAC is the main cause
of the reduced throughput. By simply increasing the data rate
of wireless links without reducing overhead, achievable
throughput gain will be limited.
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Network Layer Design
• As cognitive radios have successfully established the links for
opportunistic transmissions, the core function of a cognitive
radio network lies in network layer design, especially routing,
while other design issues such as flow control, network radio
resource management and network mobility management are
based around that routing.
• A CRN node can be considered to be a node with a dynamic
spectrum access capability and programmable multi-radio
capability.
• A CR node seeks and used the spectrum hole in multi-radio
systems to forward packets in a self-organized way.
• Prior to the routing of any CRN packets/traffic, the very first
function of CRN network layer is association, which means
cognitive radio node successfully accesses the CRN including
PS(primary system).
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Network Layer Design
• After sensing possible transmission opportunities (i.e spectrum
holes), a CR must complete association, and then execute
dynamic spectrum access(DSA) through physical layer
transmission and medium access control to send packets from
CR transmitter to CR receiver.
• The CR receiver can be a CR or a node in PS.
ROUTING AND FORWARDING:
• Routing and forwarding are the main functions of the network
layer.
• The IP modules in the hosts and the Internet routers are
responsible for delivering packets from their sources to their
destinations.
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Network Layer Design
•
•
•
•
It consists of two closely related parts:
Maintaining network topology information
Forwarding packets
Hosts and routers must learn the network topology to know
where the destinations are, by exchanging information on
connectivity and the quality of network links.
• Routing information is derived from network topology
information and stored in a data structure called routing tables
in hosts and router.
• Routing tables are created and maintained either manually or
by dynamic routing protocols.
• When there is a packet to deliver, a hosts or router consults the
routing table to find out where to forward the packet.
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Network Layer Design
• Each router relays a packet to the next hop that brings it closer
to its destination.
• FEATURES OF ROUTING IN CRN:
• Link Availability:
• CRN links are available under idle duration of the primary
system so that DSA can effectively fetch such opportunities
after successful spectrum sensing.
• Links in CRN’s, involving CR as transmitters and receivers,
allows the CRN topology to be random.
• The link available period is in the range of milliseconds, as in
wireless networking.
• Unidirectional links: Typical wireless networks have bidirectional links because radio communication is half duplex.
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Network Layer Design
• CR node just have an opportunity for transmission from the
other direction.
• A link involving CR node is likely to be unidirectional
• This distinguishes CRN from other wireless networks
especially regarding the network layer functions.
• Heterogeneous Wireless Networks:
• CRN are generally formed by heterogeneous wireless
networks(Co-existing primary systems and CR nodes to form
ad-hoc networks).
• Inter system handover is usually required for routing.
• CR links might be available for an extremely short duration
and the successful networking lies in cooperative relaying
among heterogeneous wireless networks.
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Network Layer Design
• To ensure a CRN link is available for network layer
functioning, we go back to hardware operation.
• Assume that a genie observes CRN operation for both the PS
and CR, the CR must use the spectrum hole window to
complete transmission of packets.
• A spectrum window period is denoted by
,
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Network Layer Design
• Since the link is either available for opportunistic transmission
or not available, considering the timings for the change of link
availability we can adopt an embedded continuous time
Markov chain and the rates specifying this continuous time can
be obtained from the statistics of spectrum measurement.
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Network Layer Design
• Illustrates 2-state Markov chain with fixed timing, where A
stands for “Link available” and N stands for “Link not
available”.
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Routing in cognitive radio networks
• Trusted Cognitive Radio Networking:
• We can introduce a trust mechanism in addition to typical
network security schemes.
• The security in CRN lies on the ground of end-to-end nodes
and intermediate nodes in CRN can simply forward the CR
traffic packets.
• Such a cooperative relay of packets can be facilitated as
amplify-and–forward (AF) and decode-and-forward(DF)
• Compress-and-forward(CF) cooperative networking facilitate
the security of the intermediate nodes due to mixing relay
packets and own traffic together.
• We can classify a node in CRN and traffic/control packets from
such a node into three categories:
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Routing in cognitive radio networks
• Secure: The node has executed a security check that is good
throughout the entire heterogeneous wireless networks, such as
through a public key infrastructure(PKI) check.
• A node classified as secure can be a CR
• Trusted:
• The level of security for trusted is not as effective as secure.
• CR is generally not able to complete a security check of several
rounds of the handshaking protocol within the timing window
of an available link.
• CR source code generates packets for opportunistic
transmission and the CR receiver node recognizes a CR source
node as trusted and can relay packets towards a CR sink node
via appropriate routing mechanism.
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Routing in cognitive radio networks
• Lure:
• CR node is neither secure nor trusted by its target receiving
node and is classifies as lure.
• The purpose of trust mechanism is to create a homogeneous
networking functioning environment for heterogeneous
wireless networks, and thus allow cooperative relay of packets
in spite of the opportunistic and extremely dynamic link
availability of CRN.
• Some critical issues of the CRN network layer operation:
• The CRN consists of CR and nodes from various co-existing
primary systems operate using different communication
parameters in different frequency bands in different
geographical locations. SDR inside CR is capable of
reconfigurable realization at multiple frequency bands.
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Routing in cognitive radio networks
• CR source code and CR destination node should conduct their
own end-to-end security beyond trust level by employing CRN
nodes to complete bidirectional verification.
• CRN nodes are assumed to conduct only AF or DF cooperative
relaying under the trust domain of CRN.
• Nodes in the secure domain may reject relays from trusted
nodes, which suggests that such links are not available in
trusted network routing. Similarly nodes in trusted domain may
reject connection requests from lure nodes.
• Any packet from a CR source node, once getting into a primary
system or infrastructure, follows the operation of the primary
system to get benefits from existing systems and networks.
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Routing in cognitive radio networks
• For example, CR source code wishes to relay its packet through
nearby wi-fi to access a website of the internet.
• As long as the packets from the CR source node are allowed to
the access point of the wi-fi, these packets transport as wi-fi
packets.
• A CR terminal device is capable of reconfiguring multiple
physical layer transmissions and multiple medium access
control schemes.
• General CRN operation can be summarized as shown.
• We have an infrastructure network as the core that might be the
Internet, several radio access networks(RAN) that provide
various ways to access the core infrastructure network.
• Mobile stations(MS) are associated with certain RAN
technology.
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Routing in cognitive radio networks
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Routing in cognitive radio networks
• A CR may also be a MS of a PS.
• Bidirectional links have double arrows , and all links in
primary systems will be bidirectional.
• Opportunistic links owing to CR dynamic spectrum access and
certain ad-hoc links have single arrows
• From CR source node, there are three different cooperative
paths to transport the packets.
• There are three cooperative paths to the CR sink as the final
destination.
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Routing of dynamic and unidirectional CR
links in CRN
• To conduct CRN routing over unidirectional CR links and
usually bidirectional links in the primary system, we can extend
on-demand routing protocols of MANET for CRN routing by
the following:
• Each CR link is modelled by a 2-state Markov chain,
independent of other CR links.
• Without knowing the specific PS, all the links are assumed
bidirectional and can support our routing protocol.
• Typical MANET routing algorithms are trying to isolate
unidirectional links.
• CR link might be unidirectional but reverse its direction on
network situations
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Routing of dynamic and unidirectional CR
links in CRN
• For routing in the CRN, we value one major purpose:
• To reduce the latency of traffic due to more cooperative paths
• CR sources are not able to transport packets to the CR
destination node without CRN technology.
• To forward the packet over an effective opportunistic CR link,
towards the appropriate direction.
• This matches the philosophy of reactive(on-demand) routing in
ad-Hoc networks.
• Each CR node executes routing only when there is a need (ondemand).
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CRN On-demand Routing
• The routing messages includes the following routing overhead
information:
• CR destination node IP
• CR source node IP
• Message ID
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CRN On-demand Routing
• When a new CR node or a new mobile station of the PS comes
into scenario, we may not be able immediately to acquire the IP
address, and so we can use an ID to serve .
• CRNO routing consists of three phases in operation:
• Sensing Phase
• Path discovery phase
• Table update phase
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CRN On-demand Routing
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Sensing Phase
• The CR node listens to the radio environment, spectrum
sensing of multiple co-existing systems(different frequency
bands) to update its forward path table.
• Forward path table records information regarding each
potential CR receiver , estimate of its trust on the CR code and
communication parameters to adjust the SDR.
• Each potential CR receiver is identified by an IP address
acquired from its past transmissions or by an ID designated by
the CR node.
• Communication system parameters can be obtained from
spectrum sensing to adjust the SDR.
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Path Discovery Phase
• Once the CR node originates a packet/frame to the destination
or receives a packet/frame for relay, it checks the backward
path table for any violation.
• In the case of no violation, CR node selects another CR node
from the forward path table to relay the packet/frame.
• The selection is based on the availability of CR links and the
forward path table.
• Links to the PS have the highest priority.
• When a violation happens, CR relay node seeks an opportunity
to “negative-acknowledge” the CR transmitter based on the
backward path table.
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Table Update Phase
• In addition to link selection to complete the routing, a
backward path route associated with this relay has to update as
a part of the backward path table.
• Each backward path route consists of parameters
• Such as
• Both
are to specify the operation
of co-existing multi-radio systems in CRN.
• The violation is defined as the detection of either loop
existence or dead-end existence.
•
plays its role in determining the existence of a loop.
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Table Update Phase
• Time-out to indicate that it is not possible to relay a packet is
issued to avoid dead-end which is useful information to update
the backward path-table
• When negative acknowledge cannot trace back the way to CR
source code, it is likely to be due to some permanent
unidirectional links.
• End-to-end timeout can terminate the routing and re-start a new
round of routing, avoids the permanent unidirectional link.
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Control of CRN
• Flow control of CRN:
• Flow control can happen in two types in CRN
• End-to-end flow control between the CR source code and CR
destination node is possible.
• For successful operation of CRN on-demand routing protocols,
we need flow control in the CRN network layer.
• Flow control is primarily for damage control purposes.
• Since it is not possible for us to ensure that neither dead-end or
loop happens in AODV(Ad-Hoc On-Demand distance vector)
• We have to detect these two cases and stop the CR link relaying
packets under these scenarios, so that network bandwidth is not
wasted.
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Control of CRN
• To achieve such a goal, loop detection and dead end detection
are needed and associated with routing.
• We can observe several segments and the packets are routed
from the CR source node to the CR destination node through
the following segments:
• Uplink CRN
• Co-existing multi-radio primary systems with infrastructure or
core network(such as internet) may be considered to transport
the packets quickly.
• Downlink CRN
• Cognitive Radio Relay Network(CRRN) can be considered as a
special kind of CRN consisting of pure CRs with the only
purpose of relaying packets.
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Control of CRN
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Control of CRN
• The traffic flow can be categorized as follows:
• ROUTING IN CRN BASED ON SEGMENTATION AND
DECOMPOSITION:
• For the uplink CRN, the routing will try to reach PS via
opportunistic CR links.
• When the CR relay node is in the process of selecting a
forward path, it has a tendency to select the node closer to the
PS, which is the node in RAN 1.
• The routing will leave the PS via opportunistic CR links for the
downlink CRN.
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Control of CRN
• CRN routing favours a way of forwarding packets that is
effective for co-existing multi-radio systems.
• A longer range primary system is to be favored in relaying
packets for a CR relay node enhancing CRN routing efficiency.
• END-TO-END ERROR CONTROL IN CRN:
• The conventional concept of packet error control lies in the
physical layer and data link layer
• Error control will be useful in supporting CRN functions.
• CRN routing tries to forward the packets and the CR sink
receive multiple copies of one transmitted packet, and these
copies of one packet might not be correct because no error
protection other than forward error control(FEC) is available.
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Control of CRN
• The conventional network layer requires an extremely low
packet error rate, which is warrented by physical layer FEC,
CRC check and data link control.
• For the CRN, data link control may or may not exist, and error
control between the CR source code and sink node is needed,
while re-transmissions will be minimized due to a much higher
price than wireless networks.
• We can borrow the idea from hybrid automatic request(HARQ)
to conduct the CRN network layer error control, to significantly
reduce the error control traffic significantly between the CR
source node and CR destination node.
• The challenge for HARQ in the CRN lies in the uncertain
number of copies of a packet to be received at the CR
destination node and in uncertain arrival times.
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EC 8071- COGNITIVE RADIO
UNIT-V ADVANCED TOPICS IN COGNITIVE
RADIO
Overview of security issues in cognitive radios, auction
based spectrum markets in cognitive radio networks,
public safety and cognitive radio, cognitive radio for
Internet of Things
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COGNITIVE RADIO NETWORK
SECURITY-INTRODUCTION
• CR is a revolutionary technology to alleviate the spectrum
shortage problem and brings improvements in the efficiency of
spectrum utilization.
• Successful deployment of CR networks and realization of
benefits depends on the placement of essential security
mechanisms.
• The emergence of the opportunistic spectrum sharing (OSS)
paradigm and cognitive radio technology raises new security
implications.
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TAXONOMY OF SECURITY THREATS
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OVERVIEW OF SECURITY THREADS TO
INCUMBENT COEXISTENCE
• Spectrum sharing or coexistence is an important attribute to
CR networks.
• CR networks support two types of coexistence:
• Incumbent coexistence(coexistence between primary and
secondary networks)
• Self- coexistence (coexistence between secondary networks)
• CR needs spectrum sensing to identify fallow spectrum bands,
i.e) white spaces
• Secondary users are permitted to operate in licensed bands
only on a noninterference basis to the incumbent user.
Secondary user that detect the presence of incumbent signal
immediately switch to another band
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OVERVIEW OF SECURITY THREADS TO
INCUMBENT COEXISTENCE
• On the other hand, if the secondary user detects the presence
of a secondary user, it invokes a self-coexistence mechanism
to share spectrum resources.
• There are two types of security threads to incumbent
coexistence:
• Primary user Emulation(PUE) Attack:
• A rogue secondary user attempts to gain priority over other
secondary users by transmitting signals that emulate the
characteristics of the incumbent’s signals.
• Due to the programmability of CR, it is possible to modify the
radio software of a CR to change its emission characteristics
so that they resemble those of incumbent transmitter.
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OVERVIEW OF SECURITY THREADS TO
INCUMBENT COEXISTENCE
• The potential impact of PUE attack depends on the legitimate
secondary users ability to distinguish the attackers signals and
actual incumbent signals while conducting spectrum sensing.
• Another security issue threatens the reliability of the
distributed spectrum sensing(DSS) process in CR networks.
• In DSS, individual nodes send their local sensing data to a
fusion center, which processes the data to determine a sensing
decision.
Byzantine Failures in DSS:
• May be caused by either malfunctioning sensing nodes
launching spectrum sensing data falsification(SSDF) attacks.
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OVERVIEW OF SECURITY THREADS TO
INCUMBENT COEXISTENCE
• The incorrect spectrum sensing data are reported to the fusion
center, which affects the accuracy of the sensing decision.
• The goal of DSS is to make an accurate sensing decision after
carrying out data fusion of local sensing results in the presence
of failures.
• Investigation of DSS Byzantine failures involves not only the
study of data fusion techniques, but also interplay between the
data fusion techniques and the spectrum sensing techniques.
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OVERVIEW OF SECURITY THREADS TO
SELF-COEXISTENCE
• Self-coexistence mechanisms of CR network are defined as
part of the network’s air interface.
• Here we focus on self-coexistence mechanisms of IEEE
802.22
• IEEE 802.22 is the first standard for wireless access networks
based on CR technology.
• It specifies the air interface for a wireless regional area
network that uses fallow segments of the UHF/VHF TV bands
between 54 and 862MHz.
• It is possible for a number of 802.22 cells to have overlapping
coverage areas.
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OVERVIEW OF SECURITY THREADS TO SELF-COEXISTENCE
• If non-exclusive sharing is not feasible, then 802.22 WRAN
needs to acquire spectrum resources through exclusive
spectrum
sharing
via,
the
On-demand
spectrum
contention(ODSC) protocol.
ODSC protocol:
• ODSC process enables a cell to acquire better or more
channels to support the quality of service of the admitted
workloads.
• Base station collects neighboring cells spectrum utilization
information by receiving inter-cell control messages.
• The control messages called inter-cell Beacon, are used by BS
to exchange spectrum utilization information.
• Inter-cell Beacon, are vulnerable to unauthorized modification,
as they are not protected by 802.22’s security sub-layer.
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RADIO SOFTWARE SECURITY THREADS
• The flexibility and adaptability brought by modern software,
low cost microprocessors and smart antennas have made
software defined and cognitive radios a reality.
• The emergence of software defined radio and software based
CR have brought new security threads.
• Without proper software protection mechanisms, CR are
vulnerable to a host of attacks targeting the radio software.
• The attacks in CR may include execution of malicious code,
removal of software based authentication or access control
functions, Intellectual property(IP) theft.etc…
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PRIMARY USER EMULATION ATTACK
(PUEA)
• In dynamic spectrum access environment, the PU always uses
the authorized frequency band and SU can utilize this
spectrum band when PU is not using it.
• In PUEA, the attacker generates fully similar type of signal as
PU to make an error in frequency band and to confuse SU.
• As an error, the SU, identifies the attacker as PU and vacate
the spectrum band immediately.
• This kind of attack is referred as PUEA.
• In a multi-hop channel environment, if PUEA is launched and
there is no idle channel for SU, then the call is dropped or
delayed.
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PRIMARY USER EMULATION ATTACK
(PUEA)
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PRIMARY USER EMULATION ATTACK
(PUEA)
• A dropped call results in unreliable communication and the
delayed call degrades the quality of service.
• Almost all the channels are affected by both malicious users
and greedy users.
• One of the major technical challenges in spectrum sensing, is
the problem of sensing primary user signals from secondary
user signals.
• To distinguish the two signals, existing spectrum sensing
schemes based on energy detectors, assume a naïve transmitter
verification scheme.
• Under such verification scheme, a malicious secondary user(an
attacker) can exploit the spectrum sensing process.
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PRIMARY USER EMULATION ATTACK
(PUEA)
• For instance, a PUE attacker may be as a primary user by
transmitting unrecognized signals in one of the licensed bands
thus preventing other secondary users from accessing that
band.
• There are alternative techniques for spectrum sensing, such as
matched filter and cyclo-stationary feature detection.
• Devices capable of such detection techniques are able to
recognize the intrinsic characteristics of primary user signals,
enabling them to distinguish signals from those of secondary
users.
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CLASSIFICATION OF PUE ATTACKS
• Depending on the motivation behind the attack, PUE attack
can be classified as selfish PUE attack or a malicious PUE
attack.
SELFISH PUE ATTACK:
• An attackers objective is to maximize its own spectrum usage.
• When selfish PUE attackers detects a fallow spectrum band,
they prevent other secondary users from competing for that
band by transmitting signals that emulate the signal
characteristics of primary user signals.
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CLASSIFICATION OF PUE ATTACKS
MALICIOUS PUE ATTACK:
• The objective of this attack is to obstruct the DSA process of
legitimate secondary users.
• Prevent legitimate secondary users from detecting and using
fallow licensed spectrum bands causing denial of service.
• Unlike a selfish attacker, a malicious attacker does not
necessarily use fallow spectrum bands for its own
communication purpose.
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ROBUST DISTRIBUTED SPECTRUM
SENSING
• The Byzantine failure problem can be caused by spectrum
sensing devices that are malfunctioning or carrying out
spectrum sensing data falsification attacks.
• A malfunctioning sensing terminal, is unable to conduct
reliable spectrum sensing and may send incorrect sensing
reports to the data collector.
• In an SSDF attack, a malicious secondary user, intentionally
sends falsified local spectrum sensing reports to the data
collector to make incorrect spectrum sensing decisions.
• Either case could cause interference to incumbent and result in
underutilization of fallow licensed spectrum.
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DISTRIBUTED SPECTRUM SENSING
• Carrying out reliable spectrum sensing is a challenging task for
CR.
• In a wireless channel, signal fading result in “hidden node
problem”.
• The hidden node problem, in the context of CR networks is
described as an instance in which secondary user in a CR
network is within the protection region of an operating
incumbent but fails to detect the existence of the incumbent.
• Besides the hidden node problem, it is also possible for a
secondary user to falsely detect an incumbent because of noise
or interference in the wireless environment.
• In DSS, each secondary acts as a sensing terminal that
conducts local spectrum sensing.
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DISTRIBUTED SPECTRUM SENSING
• The results are reported to a data collector(or fusion center)
that executes data fusion and determines the final spectrum
sensing
Byzantine Failure in Data fusion:
• The DSS is vulnerable to a number of security threads.
• In particular Byzantine failure is a major threat to data fusion
process.
• The Byzantine failure could be caused by either
malfunctioning sensing terminals or an SSDF attack.
• Both cases result in one or more sensing terminals sending
false local spectrum sensing reports to a data collector, causing
the data collector to make a wrong spectrum sensing decision.
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AUCTION BASED SPECTRUM MARKETS IN
COGNITIVE RADIO NETWORKS
• Access to the radio spectrum is a key requirement for
continuous wireless growth and deployment of new mobile
services.
• Fast growing demand for radio spectrum, regulators are
implementing in a much more flexible and liberal forms of
spectrum management, referred as dynamic spectrum
management.
• Spectrum trading is a market based approach for spectrum
redistribution that enables a spectrum license holder to sell or
lease all or a portion of its spectrum to a third party.
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DYNAMIC SPECTRUM MICRO-AUCTIONS
• Micro-auction mechanisms allow for the trading of spectrum
rights at the network level
• These auction mechanism could be highly attractive to
network operators: They provide a flexible and cost-effective
means for dynamic expansion of spectrum resources
• The spectrum obtained through micro-auctions can be used for
congestion relief during peak loads in traffic or enhance
existing services and provide new services without the need
for acquiring additional spectrum
• Generally users will be able to dynamically and locally vary
their operating frequencies and access the best available
spectrum on a time basis.
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DYNAMIC SPECTRUM MICRO-AUCTIONS
THE ROLE OF COGNITIVE RADIOS:
• Cognitive functionality is essential in the realization of microauctions because wireless devices can understand the
regulatory, technical and economic context within which they
perform the required negotiation and decision making tasks.
• Access technologies such as OFDMA will play important role
in enabling micro auction mechanisms.
• These technologies support dynamic bandwidth availability
and permit grouping, subdividing and pooling of pieces of the
spectrum into neatly packaged spectrum channels.
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DYNAMIC SPECTRUM MICRO-AUCTIONS
MULTIPARTY TRADING WITH SPECTRUM USE:
• Multiple providers can selectively offer their idle spectrum
pieces and each spectrum piece can be sold to multiple small
buyers.
• The new market place can exploit spectrum reusability in
spatial and temporal domains to improve spectrum usage
efficiency.
ON-DEMAND SPECTRUM TRADING:
• Instead of forcing buyers to purchase predefined spectrum
licenses, the new marketplace enables buyers to specify their
own demands.
• Such flexibility not only attracts a large number of participants
but also enables the system to effectively improve spectrum
utilization.
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DYNAMIC SPECTRUM MICRO-AUCTIONS
ECONOMIC ROBUSTNESS WITH SPECTRUM REUSE:
• Without good economic design, spectrum auctions can be
easily manipulated by bidders suffering huge efficiency loss.
• Only by preventing market manipulation, auction can attract
new entrants and efficiently distribute spectrum to make the
best use of resources.
ON-DEMAND SPECTRUM AUCTIONS:
• On demand spectrum auction must distribute spectrum on the
fly to large number of bidders
• Spectrum auctions are multiunit auctions, where the spectrum
is divided into number of identical channels for sale
• An efficient allocation algorithm is also needed to distribute
spectrum in real-time subject to the complex interference
among bidders.
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DYNAMIC SPECTRUM MICRO-AUCTIONS
BIDDING FORMAT: PIECEWISE LINEAR PRICEDEMAND (PLPD) BIDS
• Assume that there are totally k channels
•
is the set of channels assigned to bidder i
• Normalized spectrum
•
• With the PLPD, bidder i expresses the desired quantity of
spectrum at each per unit price Pi, using a continuous concave
piecewise linear demand curve.
• A simple example is a linear demand curve:
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DYNAMIC SPECTRUM MICRO-AUCTIONS
PRICING MODELS:
• Without considering economic robustness, the auction pricing
follows directly from each bidder’s bid.
• Revenue produced by each bidder is a piecewise quadratic
function
•
•
•
•
Pricing model is divided into two types,
Uniform pricing
Discriminatory pricing
Uniform pricing: The auctioneer chooses a single clearing
price p for all the winners.
• Discriminatory pricing: The auctioneer sets non-uniform
clearing prices across bidders.
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DYNAMIC SPECTRUM MICRO-AUCTIONS
LINEARIZING THE INTERFERENCE CONSTRAINTS :
• Consider two nodes i and j located at coordinates
• Node i is to the left of node j, if
• If
, then the node with the smaller index is considered
to be to the node to the left.
ECONOMICALLY ROBUST SPECTRUM AUCTIONS:
• By encouraging bidders to reveal their true valuations, a
truthful auction can help the auctioneer increase its revenue by
assigning the spectrum to the bidders who value it the most.
• An efficient and truthful spectrum auction is one that is
truthful and maximizes the efficiency of spectrum usage
subject to the interference.
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DYNAMIC SPECTRUM MICRO-AUCTIONS
DOUBLE SPECTRUM AUCTIONS FOR MULTIPARTY
TRADING:
• In addition to truthfulness and spectrum reuse, a double
spectrum auction must achieve two additional properties:
individual rationality and budget balance.
• A double auction is ex-post budget balanced if the auctioneers
profit is
• The profit is defined as the difference between the revenue
collected from buyers and expense paid to sellers.
• A double spectrum auction framework achieves the four
required properties:
• Spectrum reuse
- Truthfulness
• Individual rationality
- Budget balance
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DYNAMIC SPECTRUM MICRO-AUCTIONS
•
•
•
•
•
•
Trust consists of three components:
Grouping buyers
Determining winners
Pricing
Grouping Buyers:
TRUST groups multiple non-conflicting buyers into groups so
that buyers in each group do not conflict and reuse the same
channel.
• Determining Winners:
• For any group Gl with
buyers, the group bid
is,
• TRUST sorts the seller bids in non-decreasing order and the
buyer group bids in non-decreasing order
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DYNAMIC SPECTRUM MICRO-AUCTIONS
• Pricing:
• To ensure truthfulness, TRUST pays each winning seller m by
the kth seller’s bid and charges each winning buyer group l by
the kth buyer group’s bid
• This group price is evenly shared among the buyers in the
group l:
• With such pricing mechanism, auctioneers profit becomes
• TRUST achieves 70-80% spectrum utilization of conventional
spectrum allocation and TRUST sacrifices 50% of spectrum
utilization in exchange for economic robustness.
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PUBLIC SAFETY AND COGNITIVE RADIO
• The communication system of rescue workers should always
work even under extreme conditions.
• Current day communication system used for public safety lack
support for multimedia applications as it comes with low
budget mass market cell phones.
• Cognitive radio, is able to acquire this spectrum on the fly only
when it is needed.
• Many incompatible standards and new broadband services are
main drivers for investigating how cognitive radio can be
applied in this field.
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PUBLIC SAFETY AND COGNITIVE RADIO
• REQUIREMENTS:
• Next generation communication system for public safety will
have very extensive requirements.
• These requirements are studied and specified by commissions
such as SAFECOM in the united states and project MESA in
Europe.
• Communication Structure:
• Public safety wireless network consists of a backbone
network, base stations and handsets.
• The backbone network is used for inter-base station
communication
• Each type of node has different physical layer requirements.
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PUBLIC SAFETY AND COGNITIVE RADIO
• For instance, emergency workers carry battery powered
handsets that are energy limited.
• For each communication software to be efficient and perfect
for energy conditions, it should be reliable at all times.
• RELIABILITY:
• For emergency networks, reliability is an important issue.
• There are two kinds of reliability: robustness and security
• Robustness:
• It is the ability of a system to avoid total failure despite
unforeseen conditions or partial damage.
• A public safety communication system should always be
available, especially during large disasters.
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PUBLIC SAFETY AND COGNITIVE RADIO
• The network should have wide coverage in the whole service
area including special coverage locations like tunnels.
• The backbone of the network, should be very robust against
failure.
• Robustness can be obtained by having at least two independent
backbone connections to each base station.
• Security:
• It is the ability of a system to withstand malicious attacks.
• The communication should be secure against eavesdropping,
spoofing and jamming.
• In addition, handsets should not contain information that can
help unauthorized users access the network.
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PUBLIC SAFETY AND COGNITIVE RADIO
• BROADBAND:
• In an emergency situation a picture could say more than a
thousand words.
• Video is even more powerful in providing a clear impression
of a complicated situation.
• In next generation public safety communication equipment
will provide advanced features like sensors for biomedical and
environmental signals.
• PAGING:
• In paging communication, short, predetermined text messages
are sent to mobile devices that are very important for public
safety applications.
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PUBLIC SAFETY AND COGNITIVE RADIO
• Paging is even more important than voice communication,
used for instance to alarm firefighters.
• The advantage of such predefined messages is that they
convey a lot of meaning in very few data bits.
• Disadvantages of commercial wireless communication
networks:
• The network gets overloaded as a result the communication
network may collapse.
• When a disaster occurs, a part of the infrastructure may be
damaged.
• Commercial networks have no backup for the power supply.
• Commercial networks lack coverage in rural areas, tunnels and
metro stations.
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BENEFITS OF COGNITIVE RADIO RELATED TO
EMERGENCY CONDITION
• Cognitive radio is a smart device that does all kinds of useful
things for its owner, based on sensory input and machine
learning.
• It is a radio that can opportunistically use white space in
licensed bands without causing interference.
• There are several benefits of cognitive radio technology.
• Communication with other networks:
• Currently there exists multiple public safety standards.
• When a large disaster occurs at the border of countries, these
countries face huge challenge if they use different
technologies.
• Cognitive radio will support for military standards and other
public safety standards.
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BENEFITS OF COGNITIVE RADIO RELATED TO
EMERGENCY CONDITION
• Backwards compatibility:
• Because of large investments and relatively small market,
legacy systems are replaced slowly and coexists with new
communication networks for a long time.
• Cognitive radio allows an upgrade of the existing equipment to
this new release without replacing the hardware.
• Introduction of New Services:
• New services could be enabled more easily by cognitive radio.
• As it can adjust its parameters according to the requirements of
the new service without any limitations.
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BENEFITS OF COGNITIVE RADIO RELATED TO
EMERGENCY CONDITION
• Improved Reliability:
• A Cognitive radio always tries to minimize interference to
other networks by changing its frequency if other signals are
present.
• Adaptability feature automatically makes a cognitive radio
more resilient to jamming.
• Enabling Broadband:
• In case of emergency, public safety networks are heavily used
and there is demand for more capacity.
• Implementing the whole network would be very costly.
• A different approach to sense empty frequency bands(white
space) and use it as a secondary user is to set up an auxiliary
communication network.
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PUBLIC SAFETY COMMUNICATIONSTANDARDS
• Several communication standards have been developed for
public safety applications.
• P25(APCO Project 25), TETRAPOL and TETRA.
• APCO Project 25 systems are used by the federal, state and
local public safety agencies in North America.
• TETRAPOL is one of the first digital public safety standards
developed in France, used by French Gendarmerie Nationale
• Terrestrial trunked mobile radio (TETRA) communication
network was developed and is used in European countries.
• TETRA:
• It is known as trans-European trunked radio. TETRA was
specifically designed for use by government agencies,
emergency services(police forces, fire dept.and ambulance)
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PUBLIC SAFETY COMMUNICATIONSTANDARDS
•
•
•
•
•
•
•
TETRA system supports several types of data communication:
Status messages
Short data services
Packet data
Circuit switched data communication
Uses TDMA with four user channels on one radio carrier
Both point-to-point and point-to-multipoint transfer can be
used.
• All voice and data traffic is protected by encryption
C2000:
• The public safety communication network in Netherlands is
called C2000.
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PUBLIC SAFETY COMMUNICATIONSTANDARDS
• T2000:
• A TETRA based network for voice and low rate data
communication uses the frequency band 380-385 MHz for
uplink and 390-395 MHz for downlink communication.
• Uses both direct mode and trucked radio mode.
• For special coverage locations like tunnels and stadiums, the
system has additional low power base stations.
• To facilitate helicopters and airplanes, TETRA AGA(airground-air services) was constructed.
• P2000:
• Paging is a very important communication application in public
safety, where short predetermined text are transmitted and
displayed on pager devices.eg. Used as alarm for firefighters
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PUBLIC SAFETY COMMUNICATIONSTANDARDS
• M2000:
• It is a software system used in public safety answering
point(PSAP).
• It is a call center responsible for answering calls to an
emergency phone number for police, firefighting and
ambulance services.
• It helps to identify which resources should be allocated to
emergency.
• It acts as an help desk in talk groups and also monitors it to use
in network management and network planning.
• C2000 network fulfills all important public safety but lacks
support for multimedia/broadband internet communication.
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APPLICATION OF COGNITIVE RADIO
• The requirements for the next generation system include
features that require broadband communication.
• Cognitive radio provide a means to find the required bandwidth.
• Public safety agencies have desperately needed additional
spectrum allocation to ease frequency congestion and enhance
interoperability.
• These problems can be mitigated through the use of cognitive
radio technology.
• For emergency and public service providers, major part is
spectrum sharing which helps in maintaining call priority and
response time.
• CR improves interoperability between different frequencies and
modulation formats.
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APPLICATION OF COGNITIVE RADIO
• Cognitive radio technology caught the attention of the US
department of justice which employed the national public safety
telecommunications council(NPSTC) to aid with public safety
communication issues.
• NPSTC effort focuses to expand spectrum allocation and reuse
• CR can prove to be more effective by utilizing some of the
existing spectrum that is not widely used.
• PROPAGATION CONDITIONS:
• Determine how far a radio wave propagates.
• A high path loss allows more spectrum reuse.i.e spectrum usage
increases.
• For CR, a high path loss is better, because it reduces the area in
which interference can be caused.
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APPLICATION OF COGNITIVE RADIO
• SYSTEM SPECTRAL EFFICIENCY:
• A wireless communication system should use the spectrum in
an efficient manner.
• System spectral efficiency can be defined as,
•
•
•
•
R – Bit rate
B – Bandwidth
K – Cluster size
R/B can be considered as the link spectral efficiency
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APPLICATION OF COGNITIVE RADIO
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APPLICATION OF COGNITIVE RADIO
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APPLICATION OF COGNITIVE RADIO
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APPLICATION OF COGNITIVE RADIO
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IOT
• IOT integrates the interconnectedness of human culture – out
things with the interconnectedness of our digital information
system – the internet
• Four stages of IOT:
• Stage 1: Networked things(wireless sensors and actuators)
• IOT should be equipped with sensors and actuators thus giving
the ability to emit, accept and process signals.
• Stage 2 (Sensor data aggregation systems and analog to
digital data conversion)
• Data from the sensors starts in analog from which needs to be
aggregated and converted into digital streams.
• Process the enormous amount of information collected on the
previous stage and optimize it.
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IoT
• Stage 3 (Edge Analytics)
• Once IoT data has been digitized and aggregated, it requires
further processing before it enters data fusion.
• For example: machine learning and visualization technologies.
• Stage 4(Analysis, Management, and storage of data at
cloud Analytics)
• Data that need more in-depth processing get forwarded to
physical data centers or cloud-based systems.
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Cognitive radio for IoT
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Cognitive radio for IoT
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Cognitive radio for IoT
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COGNITIVE IOT FRAMEWORKS
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COGNITIVE IOT FRAMEWORKS
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COGNITIVE IOT FRAMEWORKS
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STANDARDIZATION EFFORTS IN CR BASED IOT
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SECURITY AND PRIVACY RELATED TO CR-IOT
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SECURITY AND PRIVACY RELATED TO CR-IOT
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