Building Cognitive Radio Networks

1
Building CognitiveRadio 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
• ltevans_at_ittc.ku.edugt

2
Building Cognitive Radios

• Introduction and Motivation
• Example implementation KU Agile Radio
• Cognitive Radio
• Rethinking design

3
KU Agile Radio
4
KU Agile Radio Concept
Digital Board andControl Processor
Power Supply
RF Transceiver
7 H x 3 W x 6 D
5
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

6
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

7
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

8
KUAR 5 GHz RF Transceiver
9
Tx/Rx Antenna Patch
10
KU Agile Radio Version 3.0

• Complete package
• Version 3.0 digital board with CP and RF boards

11
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

12
Cognitive Radios
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Cognitive Radio Learning Structure
Hours
Milli-Seconds
Minutes/Hours
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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

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Cognitive Radio Learning Structure
Hours
Milli-Seconds
Minutes/Hours
16
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

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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

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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

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Cognitive Radio Learning Structure
Hours
Milli-Seconds
Minutes/Hours
20
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

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Cognitive Radio Software Architecture
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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 andtransmission
• Adding network routes

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Cognitive Radio Software Architecture
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Cognitive Parameters

• General Radio Model
• Every processing stage is programmable and
  controllable

• Transmission parameters (Knobs)
• Transmit power
• Modulation
• Code rate
• Symbol rate
• Frame length
• Environmental parameters (Dials)
• SNR
• Path loss
• Battery life
• Delay spread

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Goal Conflicts
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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

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Genetic Algorithms

• 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

• 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

• 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?

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ReTargetA Radio Design Framework
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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.
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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 tohow a
component is implemented.
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ReTarget Design Flow
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ReTarget Tool Organization
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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

34
KU Agile Radio Team
35
Building CognitiveRadio 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
• ltevans_at_ittc.ku.edugt