Cognitive Radio

1. Cognitive Radio Jeff Reed reedjh@vt.edu reedjh@crtwireless.com (540) 231 2972 James Neel James.neel@crtwireless.com (540) 230-6012www.crtwireless.com CERDEC February 5, 2008  Cognitive Radio Technologies, 2008

2. Jeffrey H. Reed • Director, Wireless @ Virginia Tech • Willis G. WorcesterProfessor, Deputy Director, Mobile and Portable Radio Research Group (MPRG) • Authored book, Software Radio: A Modern Approach to Radio Engineering • IEEE Fellow for Software Radio, Communications Signal Processing and Education • Industry Achievement Award from the SDR Forum • Highly published. Co-authored – 2 books, edited – 7 books. • Previous and Ongoing CR projects from • ETRI, ONR, ARO, Tektronix • Email: reedjh@vt.edu  Cognitive Radio Technologies, 2008

3. James Neel • President, Cognitive Radio Technologies, LLC • PhD, Virginia Tech 2006 • Textbook chapters on: • Cognitive Network Analysis in • Data Converters in Software Radio: A Modern Approach to Radio Engineering • SDR Case Studies in Software Radio: A Modern Approach to Radio Engineering • UWB Simulation Methodologies in An Introduction to Ultra Wideband Communication Systems • SDR Forum Paper Awards for 2002, 2004 papers on analyzing/designing cognitive radio networks • Email: james.neel@crtwireless.com  Cognitive Radio Technologies, 2008

4. Presentation Overview • (22) Introductory Material • Definitions, applications • (76) Implementation Issues • Architectures, sensing, classification, decisions • (39) Networking Issues • Problems and different approaches to mitigate those problems • (14) Ongoing Efforts • Commercial, University, Military • (19) Conclusions  Cognitive Radio Technologies, 2008

5. What is a Cognitive Radio? Concepts, Definitions  Cognitive Radio Technologies, 2008

6. Cognitive Radio: Basic Idea • Software radios permit network or user to control the operation of a software radio • Cognitive radios enhance the control process by adding • Intelligent, autonomous control of the radio • An ability to sense the environment • Goal driven operation • Processes for learning about environmental parameters • Awareness of its environment • Signals • Channels • Awareness of capabilities of the radio • An ability to negotiate waveforms with other radios Waveform Software Software Arch Services Control Plane OS Board APIs Board package (RF, processors)  Cognitive Radio Technologies, 2008

7. Cognitive Radio Capability Matrix  Cognitive Radio Technologies, 2008

8. Why So Many Definitions? • People want cognitive radio to be something completely different • Wary of setting the hype bar too low • Cognitive radio evolves existing capabilities • Like software radio, benefit comes from the paradigm shift in designing radios • Focus lost on implementation • Wary of setting the hype bar too high • Cognitive is a very value-laden term in the AI community • Will the radio be conscious? • Too much focus on applications • Core capability: radio adapts in response changing operating conditions based on observations and/or experience • Conceptually, cognitive radio is a magic box  Cognitive Radio Technologies, 2008

9. OODA Loop: (continuously) Observe outside world Orient to infer meaning of observations Adjust waveform as needed to achieve goal Implement processes needed to change waveform Other processes: (as needed) Adjust goals (Plan) Learn about the outside world, needs of user,… Conceptual Operation Cognition cycle [Mitola_99] Infer from Context Orient Infer from Radio Model Establish Priority Normal Pre-process Select Alternate Goals Parse Stimuli Plan Urgent Immediate Learn Observe New States Decide States User Driven (Buttons) Generate “Best” Waveform Autonomous Outside World Act Allocate Resources Initiate Processes Negotiate Protocols  Cognitive Radio Technologies, 2008

10. Typical Cognitive Radio Applications What does cognitive radio enable?  Cognitive Radio Technologies, 2008

11. Bandwidth isn’t scarce, it’s underutilized Measurements averaged over six locations: • Riverbend Park, Great Falls, VA, • Tysons Corner, VA, • NSF Roof, Arlington, VA, • New York City, NY • NRAO, Greenbank, WV, • SSC Roof, Vienna, VA ~25% occupancy at peak  Cognitive Radio Technologies, 2008 Modified from Figure 1 in Published August 15, 2005 M. McHenry in “NSF Spectrum Occupancy Measurements Project Summary”, Aug 15, 2005. Available online: http://www.sharedspectrum.com/?section=nsf_measurements

12. Random Access Primary Signals TDMA Opportunistic Signals Conceptual example of opportunistic spectrum utilization  Cognitive Radio Technologies, 2008

13. RF components are expensive Cheaper analog implies more spurs and out-of-band emissions Processing is cheap and getting cheaper Cognitive radios will adapt around spurs (just another interference source) or teach the radio to reduce the spurs Better radios results in still more available spectrum as the need arises. Likely able to exploit SDR Cognitive radio permits the deployment of cheaper radios  Cognitive Radio Technologies, 2008

14. Improved Link Reliability • Cognitive radio is aware of areas with a bad signal • Can learn the location of the bad signal • Has “insight” • Radio takes action to compensate for loss of signal • Actions available: • Power, bandwidth, coding, channel, form an ad-hoc network • Radio learns best course of action from situation Signal Quality Good Transitional Poor • Can aid cellular system • Inform system & other radios of identified gaps  Cognitive Radio Technologies, 2008

15. Automated Interoperability • Basic SDR idea • Use a SDR as a gateway to translate between different radios • Problems • Which devices are present? • Which links to support? • With SDR some network administrator must answer these questions • Basic CR idea • Let the cognitive radio observe and learn from its environment in an automated fashion.  Cognitive Radio Technologies, 2008

16. Spectrum Trading • Underutilized spectrum can be sold to support a high demand service • Currently done in Britain • Permitted in US among public safety users • Currently has a very long time scale (months) • Faster spectrum trading could permit for significant increases in available bandwidth • How to recognize need and availability of additional spectrum? • Environment + context awareness + memory  Cognitive Radio Technologies, 2008

17. Collaborative Radio • A radio that leverages the services of other radios to further its goals or the goals of the networks. • Cognitive radio enables the collaboration process • Identify potential collaborators • Implies observations processes • Classes of collaboration • Distributed processing • Distributed sensing  Cognitive Radio Technologies, 2008

18. Concept: Leverage other radios to effect an antenna array Applications: Extended vehicular coverage Backbone comm. for mesh networks Range extension with cheaper devices Issues: Timing, mobility Coordination Overhead Cooperative Antenna Arrays Cooperative MIMO Second Hop First Hop First Hop First Hop First Hop First Hop First Hop Relay cluster Relay cluster Relay cluster Relay cluster Relay cluster Relay cluster Destination Cluster Source Cluster Source Cluster Source Cluster Source Cluster Source Cluster Source Cluster Transmit Diversity destination  Cognitive Radio Technologies, 2008 source

19. Distributed processing Exploit different capabilities on different devices Maybe even for waveform processing Bring extra computational power to bear on critical problems Useful for most collaborative problems Collaborative sensing Extend detection range by including observations of other radios Hidden node mitigation Improve estimation statistics by incorporating more independent observations Immediate applicability in 802.22, likely useful in future adaptive standards Other Opportunities for Collaborative Radio (1/3)  Cognitive Radio Technologies, 2008

20. Improved localization Application of collaborative sensing Security Friend finders Reduced contention MACs Collaborative scheduling algorithms to reduce collisions Perhaps of most value to 802.11 Some scheduling included in 802.11e Other Opportunities for Collaborative Radio (2/3)  Cognitive Radio Technologies, 2008

21. Distributed mapping Gather information relevant to specific locations from mobiles and arrange into useful maps Coverage maps Collect and integrate signal strength information from mobiles If holes are identified and fixed, should be a service differentiator Congestion maps Density of mobiles should correlate with traffic (as in automobile) congestion Customers may be willing to pay for real time traffic information Theft detection Devices can learn which other devices they tend to operate in proximity of and unexpected combinations could serve as a security flag (like flagging unexpected uses of credit cards) Examples: Car components that expect to see certain mobiles in the car Laptops that expect to operate with specific mobiles nearby Other Opportunities for Collaborative Radio (3/3)  Cognitive Radio Technologies, 2008

22. Cognitive radio evolves the software radio concept to permit intelligent autonomous adaptation of radio parameters Significant variation in definitions of “cognitive radio” Question of how “cognitive” the radio is Numerous new applications enabled Opportunistic spectrum utilization, collaborative radio, link reliability, advanced network structures Differing implementation approaches Many applications implementable with simple algorithms Greater flexibility achievable with a cognitive engine approach Many objectives will require development of a cognitive language In a network, adaptations of cognitive radios interact Interaction can be mitigated with policy, punishment, cost adjustments, centralization or potential games Commercial implementations starting to appear 802.22, 802.11h,y, 802.16h And may have been around for a while (cordless phones with DFS) Summary  Cognitive Radio Technologies, 2008