International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
Efficient Utilization of Spectrum in Cognitive Networks by
Using Femtocell.
Bhaskaruni Ramalakshmi (Student),
Modadugu Rangababu(Student),
Padyala Varaprasad (Ast.Professor)
Department Of Computer Science and Engineering, K L University
Vaddeswaram, GunturDt., AndhraPradesh, India.
Abstract
Keywords: Cutting edge, volatile,
femtocell, zero knowledge protocol.
In communication networks, cognitive network
is a new type of data network that makes use of
cutting edge technology from several research
areas to solve some problems current networks
are faced with. Cognitive MACs are becoming a
reality for efficiently utilizing the constrained
spectral resources. The spectrum opportunities
are volatile and often difficult to predict. In this
paper we propose the cognitive networks are the
secondary users are going to use the spectrum
which is free in a particular time slot. These
secondary users are going to use the unused
space without affecting the primary users. The
femtocell is small base station; with this we will
reuse the unused spectrum frequency in
uncovered region also. While exchanging the
sensed information by the nodes we are going to
use zero knowledge protocol for security
purpose. Femtocell connects to the service
provider’s network via broadband current
designs typically support two to four active
mobile phones in a residential setting, and eight
to 16 active mobile phones in enterprise settings.
A femtocell allows service providers to extend
service coverage indoors or at the cell edge,
especially where access would otherwise be
limited or unavailable. In cryptography, a zeroknowledge proof or zero-knowledge protocol is
a method by which one party can prove to
another that a statement is true, without
allowing the other party to prove the statement
to anyone else. A zero-knowledge proof must
necessarily require interactive input from the
verifying party; otherwise the verifier could
simply record the proof and replay it to
convince someone else.
ISSN: 2231-5381
I.
predict,
Introduction
We consider a cognitive radio network with one
primary user and multiple secondary users. Packets
randomly arrive at the primary user and are queued
for transmission. The primary user transmits on
every slot that it has packets. The success
probability is determined by the cooperation
decisions made by the secondary users. [1] In CR
networks, channel sensing and spectrum access are
tightly coupled to the medium access—no matter if
it is through random access, time slotted principle
or hybrid scheme. In recent years, a number of
spectrum agile and
Cognitive MAC protocols have been proposed.
These solutions have different requirements for
network infrastructure and hardware capabilities of
Secondary Users (SUs). This would allow
secondary users to identify available spectrum
resources and communicate in a manner that limits
the level of interference perceived by the primary
users. Traditional protocols for the identification of
parties in a transaction suffer from flaws that are
inherent to the process used to achieve the
objective. In simple password protocols, a claimant
A gives his password to a verifier B. If certain
precautions are not taken, an eavesdropper can get
hold of the password that was transferred, and from
there on he can impersonate A to his liking. [2].
While simplifying the design of OSA networks,
continuous full-spectrum sensing is energy
inefficient and hardware demanding, especially for
low-cost battery-powered wireless nodes with
busty traffic. We assume instead that each
secondary user can choose to sense a subset of the
possible channels (only when it has data to
transmit) and must decide whether transmission is
possible based on the sensing outcome. [3] Inspired
by the cognitive radio technology which enables a
station to cognize and adapt to the communication
environment to reach the optimum network
performance. However, since an enormous number
of femto-networks can be expected to overlay the
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Macro-network, the computational complexity and
large among of scheduling related information
exchanges are challenges in such centralized
scheme. As a result, a radio resource management
scheme for each femtonetwork shall be able to
“autonomously” utilize the radio resources not
occupied by the Macro-network so as to mitigate
interference while providing QoS guarantees. To
combat the fading channel (both small scale fading
and large scale fading) and interference from the
Macro-network, the truncated channel inversion
power control is chosen as the power control
scheme in the femto-network. As a result, the
signal to interference and noise power ratio (SINR)
of each resource block can be maintained at a
requirement value. [4] Although some works have
been done for heterogeneous cognitive radio
networks with femtocells, most of previous works
are focused on spectrum sharing and interference
avoidance. Consequently, the energy efficiency
aspect in this setting is largely ignored. In addition,
most of previous works assume that only the
femtocell base station has the cognitive capability,
without considering the cognitive capability of the
macrocell base station. There is a growing body of
work that investigates alternate models for the
interaction between the primary and secondary
users in a cognitive radio network. In particular, the
idea of cooperation at the physical layer has been
considered
from
an
information-theoretic
perspective in many works. These are motivated by
the work on the classical interference and relay
channels. The main idea in these works is that the
resources of the secondary user can be utilized to
improve the performance of the primary
transmissions. In return, the secondary user can
obtain more transmission opportunities for its own
data when the primary channel is idle. These works
mainly treat the problem from a physical
layer/information theoretic perspective and do not
consider upper layer issues such as queuing, higher
priority for primary user, etc.
exploit the opportunities to transmit with increased
throughput. With its remembrance capability, it can
arrange a new call by a different carrier for those
locations where calls with existing services drop.
The fundamental features of CR technology have
been shown in Fig.2. CR network serves as a
framework in accessing the spectrum allocation
dynamically, and spectrum opportunity deals with
the usage of a free channel that is part of radio [8].
II. DESIGN AND IMPLEMENTATION
Fig.3 shows the spectrum usage by PUs and the
formation of free channels (also called white
spaces). These free channels are in fact the
opportunities for SUs to transmit. MAC protocols
deal with the exchange of this type of information
in the form of free channel list (FCL) amongst CR
nodes. MAC protocols for CR networks can be
broadly classified as centralized and decentralized.
The centralized MAC protocols use a central entity,
usually called a base station, which is responsible
for detection, coordination and communication of
multiple cognitive devices in a cognitive radio
network [8]
Cognitive radio (CR) technology is the solution to
the shortage of spectrum and the inefficiency of its
utilization. CR nodes are intelligent wireless
devices that sense the environment, observe the
network changes, and then use knowledge learnt
from the previous interaction with the network and
make intelligent decisions to seize the opportunities
to transmit. This process of scanning the spectrum
(S), exchanging control information (E), agreeing
upon white space (A) and transmitting data (T) on
the network is repeated continuously in a cycle
called SEAT cycle. Fig.1 shows how a cognitive
radio learns from its environment and tunes its
transceivers to adapt the network changes. CR can
autonomously detect the unoccupied spectrum and
ISSN: 2231-5381
Fig 1: SEAT Cycle
Fig2: Fundamental features of a cognitive radio
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
form of a frame-train. Since longer occupancy of
the spectrum by a cognitive user may cause a clash
with the primary owner of the spectrum, the
protocol shortly listens to the medium between two
consecutive frame transmissions to ensure the
availability of the spectrum. [6]
Fig3: Spectrum usage by PUs and the formation of whitespaces.
Dynamic channel selection is performed before
packet transmissions and opportunistic utilization
of an available channel is accomplished. Nodes are
able to communicate with each other even without
having any prior knowledge about the sensed
spectrum characteristics of other nodes in the
network. However in practice, nodes exchange the
sensed channel characteristics (compressed in a
binary format) inside the preamble sequence, which
is transmitted before data. Channel maps of
neighbours allow a node to avoid spatially local
interferers as will be demonstrated. In each sensing
cycle, a node scans the available channels
sequentially for any potential spectral activity and
updates the history and the associated channel
weights. During the sensing operation, a node is
also able to characterize the interferers, their
strengths and occupancy levels. We use a heuristics
based method for channel selection. Without
compromising the baseline operation, any learning
algorithm predicting the spectrum occupancy can
be plugged in to the protocol for improving channel
selection. Different components of the spectrum
sensing and channel selection methods are shown
in Fig.4. A transmitting node sends a repeated
sequence of short preamble frames using the base
rate modulation in the selected channel followed by
data. Each frame contains control information such
as the destination address, source address, timing
offset for the data frame, the modulation scheme
for data transmission and its own channel
characteristics map. Fig.4 illustrates the various
components of the MAC involving a packet
transmission. A non-addressed node is also able to
gather the spatial spectral characteristics of the
transmitting node and other relevant meta-data by
overhearing a preamble frame. If a unicast
transmission is taking place, other nodes in the
neighbourhood can also simultaneously utilize an
available spectrum hole for data communication,
resulting in higher capacity. In order to support
variable data traffic volumes, the protocol is able to
transmit multiple data frames back to back in the
ISSN: 2231-5381
Fig 4: Simplified component diagram involving a packet
transmission
III. Proposed System
In this paper I propose the cognitive networks are
the secondary users are going to use the spectrum
which is free in a particular time slot. These
secondary users are going to use the unused space
without affecting the primary users. For the usage
of unused space by using femtocell. The femtocell
cell is small base station; with this we will reuse
the unused spectrum frequency in uncovered region
also. While exchanging the sensed information by
the nodes we are going to use zero knowledge
protocol for security purpose.
IV. Remarks and observations
It is clear that the zero knowledge property and the
soundness property have no say in the level of
security that a system presents. It is of key
importance to the security of a given protocol for it
to depend on computationally difficult problems.
No proofs exist for the most commonly used
problems (e.g., integer factorization, knapsack
problem, discrete logarithm, etc.), so the securities
of the systems that use them are directly dependent
on future developments in the field of
Computational Complexity. [2]
V. CONCLUSIONS AND FUTURE WORK
In Stage I, the primary network offers the spectrum
selling price to the cognitive base station. In Stage
II, the cognitive base station decides to buy the
spectrum size from a primary network and allocates
the spectrum to femtocells or macro secondary
users. In Stage III, the femtocell base station
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performs power allocation for the femtocell
secondary users. Then we have used the backward
induction method to solve the resource allocation
and proved the existence and uniqueness of the
Stackelberg game equilibrium. Our protocol
dynamically selects
an interference minimal channel using a distributed
channel selection strategy. An available wireless
channel can further be utilized in an opportunistic
fashion with only a little overhead for potentially
subsequent transmissions by keeping the nodes
listening for a short duration to the available
channel. Performance evaluation experiments
conducted on a WARP testbed show that our
protocol is able to deliver packets with high
reliability and throughput in
interfering
environments. On the contrary, a random channel
selection scheme shows very low packet delivery
ratios.
5.
6.
7.
8.
Femtocell Networks by Shao-Yu Lien in
2010.
SWITCH: A Multichannel MAC Protocol
for Cognitive Radio Ad Hoc Networks by
Mohamed A. Kalil, Andr´e Puschmann
and Andreas Mitschele-Thiel .
Demo Abstract: A Decentralized MAC
Protocol for Opportunistic Spectrum
Access in Cognitive Wireless Networks
by Junaid Ansari, Kackertstrasse 9, D52072, Aachen.
Text
Book
Mobile
cellular
Telecommunications by william c.y.lee.
An Analysis on Decentralized Adaptive
MAC Protocols for Cognitive Radio
Networks By Munam Ali Shah Sijing
Zhang Carsten Maple in 2013.
References:
1. Opportunistic Cooperation in Cognitive
Femtocell Networks by Rahul Urgaonkar,
Michael J. Neely in 2011.
2. A Primer on Zero Knowledge Protocols
by Gerardo I. Simari in 2002.
3. Decentralized Cognitive MAC for
Opportunistic Spectrum Access in Ad Hoc
Networks: A POMDP Framework by
Qing Zhao, Lang Tong in 2007.
4. Cognitive Radio Resource Management
for QoS Guarantees in Autonomous
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