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Cognitive Radio and Public Safety Communications

Cognitive Radio and Public Safety Communications

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Cognitive Radio and Public Safety Communications

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  1. Cognitive Radio and Public Safety Communications Charles W. Bostian Virginia Tech August 8, 2006

  2. Topics • Basic Concepts of Cognitive Radio (CR) and its relationship to Software Defined Radio (SDR) • Cognitive Radio Applications • Implementing Cognitive Radios • The Big Picture • Some of the Details • Regulatory Issues in Cognitive Radio Deployment • Practical Considerations: Antennas, User Interfaces, etc.

  3. Acknowledgments Many VT faculty and students contributed slides to this presentation. Here is a partial list. Jeffrey Reed Joseph Gaddert Thomas Rondeau Kyouwoong Kim David Maldonado Kyung Bae Bin Le Lizabel Morales David Scaperoth Arul Imran Tonya Smith-Jackson Warren Stutzman

  4. My Research Sponsors and Partners This project is supported by Award No. 2005-IJ-CX-K017 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect the views of the Department of Justice. This material is based upon work supported by the National Science Foundation under Grants No. 9983463, DGE-9987586, andCNS-0519959. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

  5. The VT Cognitive Wireless Technologies Team

  6. Our local police and fire departments.

  7. Basic Concepts of Cognitive Radio (CR) and its relationship to Software Defined Radio (SDR).

  8. Software Defined Radios – how the radio is constructed and controlled Cognitive Radios – how the radio behaves Legacyradios– function is defined in hardware. Software defined radios – function is defined by software. An SDR is essentially a computer that generates and understands radio signals.

  9. We define Cognitive Radios to be flexible radios (SDRs) with a cognitive engine that is capable of:

  10. Definitions (nothing is every simple) Our working definition of a cognitive radio: an aware and intelligent entity operating a frequency and waveform agile radio (transceiver). The operating entity “turns the radio’s knobs” and “reads the radio’s meters.” The radio does not have to be an SDR, but it almost always is.

  11. The VT Cognitive Engine: A Simple Concept Radio TX Radio RX Channel Statistics “Meters” “Old Knobs Settings” “Old Knobs Settings” Cognitive Engine “Optimized Solution” “New Settings” “New Settings” Radio Parameters “Knobs and Meters”

  12. Knobs and Meters Sample tabulation of knobs and meters by layer (adapted from Prof. Huseyin Arslan)

  13. Attributes Commonly Associated with CR • Aware of RF environment, its own capabilities and limitations, FCC rules, operating etiquettes and protocols, its operator’s priorities, needs, and privileges. • Takes action to maximize some set of performance criteria subject sets of priorities and constraints • Learns by experience • Deals intelligently with situations that are completely new and unanticipated

  14. Cognitive radios are machines that sense their environment (the radio spectrum) and respond intelligently to it. • Like animals and people they • seek their own kind (other radios with which they want to communicate) • avoid or outwit enemies (interfering radios) • find a place to live (usable spectrum) • conform to the etiquette of their society (the Federal Communications Commission) • make a living (deliver the services that their user wants) • deal with entirely new situations and learn from experience

  15. Cognitive Radio History Cognitive radio began with Joseph Mitola’s work in the late 1990’s, where he visualized cognition running in the application layer. We have extended it downwards into the MAC and PHY layers. PHY = hardware settings. MAC=access to the radio spectrum.

  16. CR Research History

  17. CWT2: The Big Picture • A complete cognitive engine • multi-band, multi-mode wireless applications • A cognitive radio wireless network workbench • flexible architecture and cross-layer performance optimizations.

  18. Overview of Cognitive Radio Applications

  19. What does cognitive radio offer the public safety community? • Interoperability – all bands and many waveforms in a single, low-cost radio • Ability to utilize more spectrum in an emergency (if you want it!) • Automatic real-time radio adjustment for optimum performance

  20. An Example: The CWT2 NIJ Interoperability Project • Recognize any of 4 legacy standards • Identify known networks • Interoperate with legacy networks • Provide a gateway between incompatible networks

  21. Spectrum utilization is quite low in many bands Concept: Have radios (or networks) identify spectrum opportunities at run-time Transparently (to legacy systems) fill in the gaps (time, frequency, space) Considered Bands ISM Public Safety TV (UHF) Spectrum Utilization dBmV/m From F. Jondral, “SPECTRUM POOLING - An Efficient Strategy for Radio Resource Sharing,” Blacksburg (VA), June 8, 2004. Lichtenau (Germany), September 2001

  22. Cognitive WiFi-Like Access Points in “Vacant” TV Channels • Buy a TV-band access point in Blacksburg and take it to wherever you live • AP senses its location (GPS) and determines what TV channels should be vacant. • AP looks at “vacant” channels in turn. If it finds a TV signal or a licensed user it leaves that channel and does not return. If AP finds another cognitive AP like itself it remembers the channel for future reference. • After searching all “vacant” channels, AP establishes itself in one that is truly vacant, if possible. If necessary it goes back to channel where it saw another cognitive AP and negotiates a sharing arrangement and/or minimizes their mutual interference.

  23. Real-Time Radio Performance Adjustment • Cellular systems are plagued with coverage gaps • Cognitive radio can enhance coverage around these gaps by: • Being aware of the areas with a bad signal → learning the areas of coverage gaps • Learning the best PHY layer parameters • Taking action to compensate for loss of signal • Actions available: • Power, bandwidth, coding • Aiding cellular system • Inform system & other radios of identified gaps Signal Quality Good Transitional Poor Slide courtesy of J.H. Reed

  24. Implementing Cognitive Radios • The Big Picture • Some of the Details

  25. What we want to build is a cognitive engine: the intelligence to “turn the radio’s knobs” and “read the radio’s meters.” Like a person or an animal, it learns by experience.

  26. Awareness Sensing and Modeling Software Architecture - Theory Learning Building and retaining Knowledge Radio Hardware Adapting Evolution and Optimization

  27. Cognition Loop Implementation

  28. Cognitive Engine – Software Architecture observe Adapt Learn and reason

  29. Complete Block Diagram

  30. Our approach is radio platform independent. Any SDR can go here

  31. “Knobs” and “Meters” GNU Radio’s Universal Software Radio Peripheral (USRP)

  32. Each of the objectives has a relationship to the other objectives This makes for a complicated multi-objective optimization problem

  33. We perform the multi-objective optimization with a genetic algorithm. Encode the knobs in a chromosome Analyze the fitness with respect to each objective

  34. Our biological approach to Cognitive Radio The Wireless System Genetic Algorithm uses a genetic algorithm, which operates on chromosomes. The genes of the chromosome represent the traits of the radio (frequency, modulation, bandwidth, coding, etc.). The WSGA creatively analyzes the information from the Cognitive System Monitor to create a new radio chromosome.

  35. Genetic Algorithm Formulation - Knobs

  36. Genetic Algorithm Formulation - Knobs

  37. Genetic Algorithm Formulation - Knobs Crossover Mutation

  38. What to consider: Understanding the objective functions

  39. What to consider: How does it scale?

  40. Multi-Objective Genetic Algorithms • GAs are well-suited to solving MODM problems • Parallel analysis of many solutions in many dimensions Pareto-Optimal Front The set of non-dominated solutions

  41. Implementing Cognitive Radios • The Big Picture • Some of the Details

  42. First Proof of Concept Results – Hardware Testbed • Adapting with limited range of adaptable parameters: • Modulation: QPSK, QAM8, QAM16 • Power: 6 dBm – 17 dBm • Frequency: See figure on left • Uplink/Downlink ratio Interfering Unit Even with this limited-flexibility legacy radio, we can use our cognitive processes to adapt the radio, including the avoidance of an interferer. Network Base Station Unit Network Subscriber Unit Frequency Channels Available to Proxim Tsunamis Interference Test setup

  43. Cognitive Radio Testbed Demo Broadband Wireless Link Interferer Cognitive Radio testbed link Interferer degrades broadband wireless link WSGA evolves radio operation Interferer Tx=11dBm QAM 16 Fibs 12 3/4 FEC Freq=b-5 Link Tx=6dBm QAM 16 Fibs 22 3/4 FEC Freq=a-5 Link Tx=16dBm QPSK 4 Fibs 4 1/2 FEC Freq=a-5 Link quality of service (QOS) is restored.

  44. Key paper: T.W. Rondeau, B.Le, C.J. Rieser, and C.W. Bostian, “Cognitive Radios with Genetic Algorithms; Intelligent Control of Software Defined Radios,” Software Defined Radio Forum, Phoenix, AZ, Nov. 15-18, 2004. Try an online demonstration at

  45. How does the cognitive engine control an SDR?

  46. The Three-domain Kingdom Radio Domain • Policy domain • Legality and security • Location, frequency and time • User domain • Operation preference • Service requirement • Radio domain • RF environment • Hardware platform Cog. Radio Technology Policy Domain User Domain

  47. Bridge between AI and Radio Hardware • We all see the problems in wireless (spectrum, interoperability, poor RRM) • Good pictures from computer science world • “Not yet”s from radio hardware engineers • A middle layer of awareness and adaptation serves as the bridge between theoretical AI and practically limited hardware capability  

  48. Cognitive Engine Concept for CR Technology • Software solution for radio cognition • Hardware-independent implementation approach • Layered and modularized design

  49. Egg Model of Radio-domain Cognition • Two layers • Modularized • API

  50. Complete Block Diagram