Building Cognitive
Radio Networks
Prof. Joseph B. Evans
Prof. Gary J. Minden
The University of Kansas
Information and Telecommunications Technology Center
2335 Irving Hill Road
Lawrence, KS 66045
<evans@ittc.ku.edu>
Building Cognitive Radios
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•
•
•
Introduction and Motivation
Example implementation – KU Agile Radio
“Cognitive” Radio
Rethinking design…
(-100dBm)
AD8347
I-Q DEMODULATOR
800 MHz - 2.7 GHz
MGA-86576
Gain = +19dB@6.0 GHz
P1dB = +4.3dBm@6.0 GHz
NF =1.8dB@6.0 GHz
+8dBi
HMC488MS8G
CLoss = 10 dB
P1dB = +8 dBm
LO Drive 0dBm
5.250-5.850 GHz
(-77dBm)
-3dB
LNA
-4dB
R
-10dB
-5dB
0-30dB RX
Attenuation
Control
Active RX Antenna Module
BASEBAND I
0
90
I
L
+19dB
-5dB
1.850-2.450 GHz
(-81dBm)
(-66dBm)
LNA
+19dB
GAIN
CONTROL
3.4 GHz
I DET
DPB Input
+3.3
AD8347
AD8349
ADF4360
ADF4113
SMV3300A
FOX801BE-160
BGA2031
MGA-83563
MGA-82563
MGA-545P8
MGA-86576
HMC488MSG8
ERA-1SM
4 @60mA
+5
80
150
MGA-82563
Gain = +10dB@4.0 GHz
P1dB =+17dBm@4. 0 GHz
240
15
20
2 @25mA
2 @50mA
3 dB
POWER
DIVDER
-2dB
50
100
40
770mA 440mA
ADF4360-1
2.150-2.450 GHz
RX IF GAIN
CONTROL
AD5601
6 BIT DAC
RX LO 1
FOX801BE-160
TCXO
LO 3
(+3.5dBm)
5
70
200
100
140
Q DET
Jumper
Select
SPI Bus
BASEBAND Q
ADF4360-2
1.850-2.150 GHz
MC68HC08
Microcontroller
I²C
BW=30 MHz
16.0 MHz
REF CLK OUT
ADF4113
+9dB
(+3.5dBm)
-5dB
SMV3300A
(+5dBm)
TX LO 2
Microstrip
Lumped
(+25dBm)
+8dBi
5.250-5.850 GHz
(+17dBm)
(+21dBm)
(+15dBm)
(+9dBm)
TX IF GAIN
CONTROL
ERA-1SM
Gain = +6dB@6.0 GHz
P1dB = +12dBm
(-2dBm)
(-8dBm)
3.4 GHz
R
+17dB
ADF4360-1
2.150-2.450 GHz
BASEBAND I
(+7dBm)
(+3dBm)
L
-4dB
AD8349
I-Q MODULATOR
700 MHz - 2.7 GHz
1.850-2.450 GHz
(-3dBm)
PA
-5dB
OPTIONAL
MGA-545P8
Gain = +11.5dB@5.8 GHz
P1dB = +21dBm@5.8 GHz
PSAT = +22dBm@5.8 GHz
ADF4360-2
1.850-2.150 GHz
AD5601
6 BIT DAC
Active TX Antenna Module

I
-10dB
+9dB
0
90
BW=30 MHz
-5dB
BASEBAND Q
For use w ith passi ve
anten na
MGA-83563
Gain = +17dB@6.0 GHz
P1dB = +15dBm@6.0 GHz
PSAT = +18dBm@6.0 GHz
BGA2031
Gain = +23dB@1.9 GHz (2.7 V CT RL)
G = 56dB@1.9 GHz
P1dB = +13dBm@1.9 GHz
5 GHz RF PCB Block Diagram
1
D. DePardo
1
1
27 JULY 05
KU Agile Radio
KU Agile Radio Concept
Digital Board and
Control Processor
Power Supply
RF Transceiver
7” H x 3” W x 6” D
KUAR Power Supply
• Provide 1.8 VDC,
2.5 VDC, 3.3 VDC and
5 VDC power to the radio,
separate supplies for the
digital and RF sections
• External power from
battery, vehicle, or mains
KUAR Control Processor
• Five functions: radio control;
signal processing;
configuration management;
adaptive algorithms; and
interface with wired networks.
• Intel Pentium-M; 1.4 GHz;
1 GB of RAM; 8 GB micro-disk;
100 Mbps Ethernet; USB;
VGA; Floating Point
• GPS
• Linux OS (Kernel 2.6); Full
TCP/IP protocol stack;
SSH/SSL; Web Server; NFS;
Samba
• KUAR CP fully participates in a
wired network with standard IP
services
KUAR Digital Board
• Xilinx Vertex II Pro V30;
2 PPC 405 cores;
31K logic cells;
350 MHz operation
• Analog Devices AD9777
DAC; I & Q; 160 Msps;
16-bit
• Linear Technologies
LTC2284 ADC; I & Q;
105 Msps; 14-bit
• 4 MB (1 M x 36-bit)
SRAM
DRAM
1 GB
SRAM
4 MB
Disk
8 GB
To/From
RF Board
32
2x14
Ethernet
USB
Video
GPS
Intel
Pentium-M
1.4 GHz
32
Vertix-II Pro
XC2VP30
2x16
LTC
LTC2284
105 Msps
Rx_I
DAC
AD9777
160Msps
Tx_I
Rx_Q
Tx_Q
KUAR 5 GHz RF Transceiver
(-100dBm)
+8dBi
5.250-5.825 GHz
(-77dBm)
HMC488MS8G
CLoss = 10 dB
P1dB = +8 dBm
LO Drive 0dBm
-3dB
LNA
-4dB
AD8347
I-Q DEMODULATOR
800 MHz - 2.7 GHz
MGA-86576
Gain = +19dB@6.0 GHz
P1dB = +4.3dBm@6.0 GHz
NF =1.8dB@6.0 GHz
LNA
+19dB
R
SHIFT REGISTER
+3.3
4 @60mA
I DET
MGA-82563
Gain = +8.8dB@6.0 G Hz
P1dB =+ 16. 8dBm@6. 0 GHz
240
15
20
Q DET
3 dB
POWER
DIVDER
-2dB
50
100
40
770mA 440mA
ADF4360-1
2.150-2.425 GHz
RX IF G AIN
CONTROL
AD5601
6 BI T DAC
RX LO 1
FOX801BE-160
TCXO
LO 3
(+3. 5dBm)
BASEBAND Q
ADF4360-2
1.850-2. 150 GHz
Jumper
Select
+5
80
150
BW=30 MHz
GAIN
CONTROL
3.4 GHz
5
70
200
100
140
2 @25mA
2 @50mA
0
90
I
-10dB-5dB
0-30 dB RF
ATTENUATION
CONTROL
AD8347
AD8349
ADF4360
ADF4113
SMV3300A
FOX801BE-160
BGA2031
MGA-83563
MGA-82563
MGA-545P8
MGA-86576
HMC488MSG8
ERA-1SM
BASEBAND I
L
+19dB
-5dB
Act ive RX Antenna Module
1.850-2.425 GHz
(-81dBm)
(-66dBm)
16.0 MHz
REF CLK OUT
ADF4113
+9dB
(+3. 5dBm)
-5dB
SMV3300A
SPI CONTROL
(+5dBm)
TX LO 2
Microst rip
Lumped
(+25dBm)
+8dBi
5.250-5.825 GHz
(+17dB m)
(+21dBm)
(+15dBm)
(+9dBm)
TX IF GAIN
CONTROL
ERA-1SM
Gain = +6dB@6.0 GHz
P1dB = +12dBm
(-2dBm)
(-8dBm)
3.4 GHz
(+7dBm)
(+3dBm)
L
-5dB
O PTIONAL
MGA-545P8
Gain = +11. 5dB@5.8 GHz
P1dB = +21dBm@5.8 GHz
PSAT = +22dBm@5.8 GHz
ADF4360-1
2.150-2.425 GHz
BASEBAND I
R
+17dB
AD8349
I-Q MODULATOR
700 MHz - 2.7 GHz
1.850-2.425 GHz
(-3dBm)
PA
-4dB
ADF4360-2
1.850-2.150 GHz
AD5601
6 BIT DAC
Active TX Antenna Module

I
-10dB
+9dB
0
90
BW=30 MHz
-5dB
BASEBAND Q
For use w ith passive
anten na
MGA-83563
Gain = +17dB@6.0 GHz
P1dB = +15dBm@6.0 GHz
PSAT = +18dBm@6.0 GHz
BGA2031
Gain = +23dB@1.9 GHz (2.7 V CT RL)
G = 56dB@1.9 GHz
P1dB = +13dBm@1.9 GHz
Agile Radio 2.0
5 GHz RF PCB Block Diagram
Revision:
1
D. DePardo
Page: 1 of 1
Date:1 JUNE 05
Tx/Rx Antenna Patch
5 GHz wideband integrated
LNA receiver patch
5 GHz wideband (1 GHz)
integrated patch computed
pattern
Preliminary performance
measurements
KU Agile Radio Version 3.0
• Complete package
• Version 3.0 digital board
with CP and RF boards
KU Agile Radio Enables
• Rapid service definition and deployment
• Bring new services to the public
• Dynamic service access
• Rapidly find and access available radio services
• Dynamic spectrum access
• Improve utilization of spectrum resource
• Spectrum commons/markets
• Devolve spectrum management to local regions
Cognitive Radios
Cognitive Radio Learning Structure
Hours
Milli-Seconds
~Minutes/Hours
“Cognitive” Challenges
• Mission Oriented Radio Configuration
• Develop techniques to select appropriate communications
modules to accomplish defined mission
• Self Configuring Radios
• Software should automatically determine capabilities of hardware
and use those capabilities
• Adaptation
• Change radio operation based on current environment
• ElectroSpace resource models
• Policy Adherence
• Software Architecture
Cognitive Radio Learning Structure
Hours
Milli-Seconds
~Minutes/Hours
Mission Oriented Properties
• Low probability of detection and interception
(LPD/LPI)
• Interference avoidance and rejection
• Multipath channel mitigation/exploitation
• Information assurance (jam resistance, security
enhancement, etc.)
• Communication range (e.g. foliage penetration)
• QoS requirements
• Communications capacity
• Power/energy efficiency
Consider Natural Disaster Communications
• Initial deployment
•
•
•
•
Robust communications messages must get through;
minimize first responder stress
Low capacity - perhaps voice only,
simple user devices
Low radio density - long links
Minimal power - low maintenance
• Early follow-on
•
•
•
•
Higher radio density - more time
and resources to deploy additional
radios
Medium capacity - increase data
services; use capacity to maintain
and increase robustness (e.g.
digital transmission and error
correcting codes)
Increased power
Tie into wired infrastructure
• Extended Support
•
•
•
•
Extensive data services - voice,
video, and data services
interoperate with established
infrastructure
Radio density as needed
High capacity
Power from grid
Mission Oriented Configuration
• Establish trade-offs between multiple mission goals
• Case-based reasoning
• Establish a case library of possible scenarios
• Match desired mission goals against case library
• Select closest case from library and adjust to present mission goals
• Genetic algorithms
• Establish utility function for present mission goals
• Establish a population of possible configurations
• Select “good” configuration and “inter-mingle” to make a new population;
repeat as configurations improve
• Expert systems
• Build a set of rules for defining configurations from present mission goals
Cognitive Radio Learning Structure
Hours
Milli-Seconds
~Minutes/Hours
Cognitive Radio Software Architecture
• For adaptation…
• Sense RF, network, and
communications
environment performance
• Adjust radio components
to current operating
conditions for best
performance
• Based on trade-offs
between alternative
adjustments
Cognitive Radio Software Architecture
Topology Manager
• Determine which radios should communicate
• Based on…
• Available ElectroSpace resources
• Application load (network queues)
• Adaptation (determining when to adjust)
• A connection involves…
• Allocation of ElectroSpace
• Scheduling reception and
transmission
• Adding network routes
Cognitive Radio Software Architecture
Cognitive Parameters
•
General Radio Model
•
Every processing stage is
programmable and controllable
Compression
Error Ctrl
Encrypt
Spreading
Modulation
Transmit
Thermal Noise
Information
Source
Mutual Interference
Composite
Channel
Jamming
Multipath
Information
Sink
Other
De-Compress
•
Transmission parameters (Knobs)
•
•
•
•
•
•
Transmit power
Modulation
Code rate
Symbol rate
Frame length
Environmental parameters (Dials)
•
•
•
•
SNR
Path loss
Battery life
Delay spread
Error Ctrl
Decrypt
De-Spread
De-Modulate
Every processing stage is programmable and controllable.
QoS Objectives
( Dials )
SNR
Delay
Profile
Spectral
Occupancy
Battery
Life
Cognitive Adaptation Module
Tx Power
Frequency
Coding Rate
( Knobs )
Bandwidth
Frame Size
Receive
Goal Conflicts
Reasoning/Control Approaches
• Exact Methods
•
•
Advantages: Exact optimal solution can be found
Disadvantages: Typically requires at least first derivative of a complex equation;
Time complexity (pure random)
• Heuristic Methods
•
•
Advantages: Lower complexity than exact methods; Increased flexibility with
regards to changes in the fitness equation
Disadvantages; Sub-optimal solutions
• Simulated Annealing
•
•
Advantages: Ease of implementation
Disadvantages: Only works on single solution (Local optima problem)
• Neural Networks
•
•
Advantages: Low memory usage, fast output
Disadvantages: Processing complexity, training needed, final output not traceable
(traceability is needed)
• Genetic Algorithms
•
•
Advantages: Parallel processing, well suited for large problem spaces
Disadvantages: Processing time
Genetic Algorithms
Average convergence over 50 runs
0.95
0.9
0.85
0.8
Fitness
Characteristics
• Evolves toward the better solution
• Typically requires large amounts of
processing power
• Parameters are represented as strings
of bits called chromosomes
• Genetic Algorithm selects the best
chromosomes and combines them in
hopes of creating a better generation
0.75
0.7
0.65
0.6
0.5
Adaptive Genetic Algorithm
• Normally the population of
chromosomes is randomly initialized
• If we assume a slow fading channel
we can bias the initial population with
chromosomes from a previous cycle
• We have shown this to improve the
GA convergence rate dramatically
Average
Maximum
Minimum
0.55
0
100
200
300
400
500
600
Generations
700
800
900
1000
Parameter Sensitivity
• How much influence does one parameter
have on communications?
• It is obvious that if we do not allow the
cognitive engine to adapt the power
parameter bad things happen
• What about frame length or symbol rate?
ReTarget:
A Radio Design Framework
Re-Targeting Radio Design Motivation
• The JTRS Software Communications Architecture (SCA)
describes interfaces between radio components
 We focus on the design of the programmable components
• Radio hardware platforms will evolve quickly, approximately
every 12-18 months, and be a combination of new hardware
and programmable components
 We focus on re-targeting a radio design to new platforms
Design once, use many.
Re-Targeting Radio Design Approach
• Use a specification language, Rosetta, to describe radio
components and systems of components through
composition
• Rosetta is an IEEE standards project, P1699
• Translate Rosetta designs into intermediate forms
• Similar to the organization of compliers, e.g. gcc
• Manipulate the intermediate design forms
• Optimize for power, space, specific implementation (e.g. hardware,
software, or FPGA), ...
• Generate required design description, e.g. VHDL, C
Translate from what a component does to
how a component is implemented.
ReTarget Design Flow
Rosetta Specifications
Rosetta Specifications
Re-Use Specifications
Intermediate
Forms
Intermediate
Forms
Re-Use Intermediate Forms
Target
Implementations
ASIC
FPGA
DSP
GPU
Radio #1
ReTarget
Implementations
ASIC
FPGA
DSP
GPU
Radio #2
ReTarget Tool Organization
Future Radio
• Innovate
• Encourage new approaches to radio and service delivery
• Collaborate
• Work with research agencies and industry to invest in the future
• Experiment
• Try new radios and economic approaches
• Think
• Anticipate impact of emerging technology and economic concepts
• Stewardship
• Demonstrate care of the public resource
KU Agile Radio Team
Investigators
Research Assistant Professor
Gary J. Minden
Joseph B. Evans
Alexander M. Wyglinski
Staff
Dan DePardo
Leon Searl
Students
A. Veeragandham
Tim Newman*
Jordan Guffy*
Dragan Trajkov
Ted Weidling*
Qi Chen
Dinesh Datla
Rakesh Rajbanshi *
Ryan Reed
Travis Short
Preeti Krishnan
V. Rory Petty*
Megan Lehnherr
Brian Cordill
Building Cognitive
Radio Networks
Prof. Joseph B. Evans
Prof. Gary J. Minden
The University of Kansas
Information and Telecommunications Technology Center
2335 Irving Hill Road
Lawrence, KS 66045
<evans@ittc.ku.edu>