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Building Cognitive Radio Networks

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. Introduction and Motivation

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Building Cognitive Radio Networks

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  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 <evans@ittc.ku.edu>

  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

  13. Cognitive Radio Learning Structure Hours Milli-Seconds ~Minutes/Hours

  14. “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

  15. 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

  17. 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 Consider Natural Disaster Communications

  18. 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

  19. 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

  21. Cognitive Radio Software Architecture

  22. 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

  23. Cognitive Radio Software Architecture

  24. QoS Objectives ( Dials ) Delay Profile Spectral Occupancy Battery Life SNR Cognitive Adaptation Module Frame Size Tx Power Coding Rate Bandwidth Frequency ( Knobs ) Cognitive Parameters • Transmission parameters (Knobs) • Transmit power • Modulation • Code rate • Symbol rate • Frame length • Environmental parameters (Dials) • SNR • Path loss • Battery life • Delay spread • General Radio Model • Every processing stage is programmable and controllable

  25. Goal Conflicts

  26. 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

  27. 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?

  28. ReTarget:A Radio Design Framework

  29. 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.

  30. 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.

  31. ReTarget Design Flow

  32. ReTarget Tool Organization

  33. 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 <evans@ittc.ku.edu>

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