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Cognitive Radio. Jeff Reed (540) 231 2972 James Neel (540) 230-6012 General Dynamics April 9, 2007. Jeffrey H. Reed. Director, Wireless @ Virginia Tech

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Cognitive Radio

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    1. Cognitive Radio Jeff Reed (540) 231 2972 James Neel (540) General Dynamics April 9, 2007  Cognitive Radio Technologies, 2007

    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:  Cognitive Radio Technologies, 2007

    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:  Cognitive Radio Technologies, 2007

    4. Overview of Presentation Material (1/2)  Cognitive Radio Technologies, 2007

    5. Overview of Presentation Material (2/2)  Cognitive Radio Technologies, 2007

    6. What is a Cognitive Radio? Concepts, Definitions  Cognitive Radio Technologies, 2007

    7. 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, 2007

    8. Cognitive Radio Capability Matrix  Cognitive Radio Technologies, 2007

    9. 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, 2007

    10. Used cognitive radio definition • A cognitive radio is a radio whose control processes permit the radio to leverage situational knowledge and intelligent processing to autonomously adapt towards some goal. • Intelligence as defined by [American Heritage_00] as “The capacity to acquire and apply knowledge, especially toward a purposeful goal.” • To eliminate some of the mess, I would love to just call cognitive radio, “intelligent” radio, i.e., • a radio with the capacity to acquire and apply knowledge especially toward a purposeful goal  Cognitive Radio Technologies, 2007

    11. Levels of Cognitive Radio Functionality Adapted From Table 4-1Mitola, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” PhD Dissertation Royal Institute of Technology, Sweden, May 2000.  Cognitive Radio Technologies, 2007

    12. Infer from Radio Model Infer from Context Orient Pre-process Normal Plan Parse Stimuli Learn New States Observe Decide States User Driven (Buttons) Autonomous Determine “Best” Plan Outside World Act Allocate Resources Initiate Processes Cognition Cycle Level 0 SDR 1 Goal Driven 2 Context Aware 3 Radio Aware 4 Planning 5 Negotiating 6 Learns Environment 7 Adapts Plans 8 Adapts Protocols Select Alternate Goals Generate Alternate Goals Establish Priority Immediate Normal Urgent Generate “Best” Waveform Determine “Best” Known Waveform Negotiate Negotiate Protocols  Cognitive Radio Technologies, 2007 Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10.

    13. 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, 2007

    14. Relationship Between SDR and CR Cognitive radio is a revolutionary evolution of software radio  Cognitive Radio Technologies, 2007

    15. Cognitive Radio & SDR • SDR’s impact on the wireless world is difficult to predict • “But what…is it good for?” • Engineer at the Advanced Computing Systems Division of IBM, 1968, commenting on the microchip • Some believe SDR is not necessary for cognitive radio • Cognition is a function of higher-layer application • Cognitive radio without SDR is limited • Underlying radio should be highly adaptive • Wide QoS range • Better suited to deal with new standards • Resistance to obsolescence • Better suited for cross-layer optimization  Cognitive Radio Technologies, 2007

    16. Software Radio Dynamically support multiple variable systems, protocols and interfaces Interface with diverse systems Provide a wide range of services with variable QoS Conventional Radio Supports a fixed number of systems Reconfigurability decided at the time of design May support multiple services, but chosen at the time of design How is a Software Radio Different from Other Radios? - Application Cognitive Radio • Can create new waveforms on its own • Can negotiate new interfaces • Adjusts operations to meet the QoS required by the application for the signal environment  Cognitive Radio Technologies, 2007

    17. Software Radio Conventional Radio + Software Architecture Reconfigurability Provisions for easy upgrades Conventional Radio Traditional RF Design Traditional Baseband Design How is a Software Radio Different from Other Radios?- Design Cognitive Radio • SDR + • Intelligence • Awareness • Learning • Observations  Cognitive Radio Technologies, 2007

    18. Software Radio Ideally software radios could be “future proof” Many different external upgrade mechanisms Over-the-Air (OTA) Conventional Radio Cannot be made “future proof” Typically radios are not upgradeable How is a Software Radio Different from Other Radios? - Upgrade Cycle Cognitive Radio • SDR upgrade mechanisms • Internal upgrades • Collaborative upgrades  Cognitive Radio Technologies, 2007

    19. Typical Cognitive Radio Applications What does cognitive radio enable?  Cognitive Radio Technologies, 2007

    20. 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, 2007 Modified from Figure 1 in Published August 15, 2005 M. McHenry in “NSF Spectrum Occupancy Measurements Project Summary”, Aug 15, 2005. Available online:

    21. Random Access Primary Signals TDMA Opportunistic Signals Conceptual example of opportunistic spectrum utilization  Cognitive Radio Technologies, 2007

    22. 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, 2007

    23. 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, 2007

    24. 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, 2007

    25. 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, 2007

    26. 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, 2007

    27. 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, 2007 source

    28. 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, 2007

    29. 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, 2007

    30. 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, 2007

    31. Cognitive Radio and Military Networks How is the military planning on using cognitive radio?  Cognitive Radio Technologies, 2007

    32. Drivers in Commercial and Military Networks • Many of the same commercial applications also apply to military networks • Opportunistic spectrum utilization • Improved link reliability • Automated interoperability • Cheaper radios • Collaborative networks • Military has much greater need for advanced networking techniques • MANETs and infrastructure-less networks • Disruption tolerant • Dynamic distribution of services • Energy constrained devices • Goal is to intelligently adapt device, link, and network parameters to help achieve mission objectives  Cognitive Radio Technologies, 2007 From: P. Marshall, “WNaN Adaptive Network Development (WAND) BAA 07-07 Proposers’ Day”, Feb 27, 2007

    33. Wireless Network after Next (WNaN) Program Organization Reliability through frequency and path diversity Intelligent agent cross-layer optimization  Cognitive Radio Technologies, 2007 Figures from: P. Marshall, “WNaN Adaptive Network Development (WAND) BAA 07-07 Proposers’ Day”, Feb 27, 2007

    34. DARPA’s WNAN Program WNaN Protocol Stack • Objectives • Reduced cost via intelligent adaptation • Greater node density • Gains in throughput/scalability • Leveraged programs • Control Based MANET– lowoverhead protocols • Microsystems Technology Office – RFMEMS, Hermit, ASP • xG – opportunistic use of spectrum • Mobile Network MIMO - MIMO Wideband Network Waveform • Connectionless Networks – rapid link acquisition • Disruption Tolerant Networks (DTN) – network layer protocols CBMANET Optimizing Topology CBMANET WNaN Network CBMANET WNaN MAC xG MIMO (MNM) COTS Physical MEMS (MTO)  Cognitive Radio Technologies, 2007 Other programs WNaN program Legend

    35. Overview of Implementation Approaches How does the radio become cognitive?  Cognitive Radio Technologies, 2007

    36. Weak cognitive radio Radio’s adaptations determined by hard coded algorithms and informed by observations Many may not consider this to be cognitive (see discussion related to Fig 6 in 1900.1 draft) Strong cognitive radio Radio’s adaptations determined by conscious reasoning Closest approximation is the ontology reasoning cognitive radios Implementation Classes • In general, strong cognitive radios have potential to achieve both much better and much worse behavior in a network, but may not be realizable.  Cognitive Radio Technologies, 2007

    37. Most research focuses on development of algorithms for: Observation Decision processes Learning Policy Context Awareness Some complete OODA loop algorithms In general different algorithms will perform better in different situations Cognitive engine can be viewed as a software architecture Provides structure for incorporating and interfacing different algorithms Mechanism for sharing information across algorithms No current implementation standard Brilliant Algorithms and Cognitive Engines  Cognitive Radio Technologies, 2007

    38. Information is about How the cognitive radio gets the information? Other opportunities to get information Environment (physical quantities, position, situations) • Measures temperature, light level, humidity, … • Receives GPS signals to determine position • Parses short-range wireless broadcasts in buildings or urban areas for mapped environment • Observes the network for e.g. weather forecast, reported traffic jams, …etc. Spectrum (communication opportunities) • Passively "listens" to the spectrum • Performs channel quality estimation • Spectrum information is provided by the network • Spectrum information is shared by other cognitive radios User • Observes user's applications, incoming/ outgoing data streams • Performs speech analysis Observation Sources  Cognitive Radio Technologies, 2007

    39. Orientation Processes • Gives radio significance of observations • Does multipath profile correspond to a known location? • Really just hypotheses testing • Algorithms • Data mining • Hidden Markov Models • Neural Nets • Fuzzy Logic • Ontological Reasoning  Cognitive Radio Technologies, 2007

    40. Decision Processes • Purpose: Map what radio believes about network state to an adaptation • Guided by radio goal and constrained by policy • May be supplemented with model of real world • Common algorithms (mostly heuristics) • Genetic algorithms • Simulated annealing • Local search • Case based reasoning  Cognitive Radio Technologies, 2007

    41. Learning Processes • Informs radio when situation is not like one its seen before or if situation does not correspond to any known situation • Logically, just an extension to the orientation process with • a “none of the above” option • Increase number of hypotheses after “none of the above” • Refine hypotheses and models • Algorithms: • Data mining • Hidden Markov Models • Neural Nets • Fuzzy Logic • Ontological Reasoning • Case based learning • Bayesian learning • Other proposed learning tasks • New actions, new decision rules, new channel models, new goals, new internal algorithms  Cognitive Radio Technologies, 2007

    42. Issue: How are radios “aware” of their environment and how do they learn from each other? Technical refinement: “Thinking” implies some language for thought. Proposed languages: Radio Knowledge Representation Language XML Web-based Ontology Language (OWL) Knowledge Representation  Cognitive Radio Technologies, 2007

    43. Overview of Cognitive Networking What happens when they leave the lab?  Cognitive Radio Technologies, 2007

    44. Outside World The Interaction Problem • Outside world is determined by the interaction of numerous cognitive radios • Adaptations spawn adaptations  Cognitive Radio Technologies, 2007

    45. Distributed Infinite recursions Instability (chaos) Vicious cycles Adaptation collisions Equitable distribution of resources Byzantine failure Information distribution Centralized Signaling Overhead Complexity Responsiveness Single point of failure Potential Problems with Networked Cognitive Radios  Cognitive Radio Technologies, 2007

    46. Implications • Best of All Possible Worlds • Low complexity distributed algorithms with low anarchy factors • Reality implies mix of methods • Hodgepodge of mixed solutions • Policy – bounds the price of anarchy • Utility adjustments – align distributed solution with centralized solution • Market methods – sometimes distributed, sometimes centralized • Punishment – sometimes centralized, sometimes distributed, sometimes both • Radio environment maps –”centralized” information for distributed decision processes • Fully distributed • Potential game design – really, the Panglossian solution, but only applies to particular problems  Cognitive Radio Technologies, 2007

    47. Cognitive Networks • Rather than having intelligence reside in a single device, intelligence can reside in the network • Effectively the same as a centralized approach • Gives greater scope to the available adaptations • Topology, routing • Conceptually permits adaptation of core and edge devices • Can be combined with cognitive radio for mix of capabilities • Focus of E2R program R. Thomas et al., “Cognitive networks: adaptation and learning to achieve end-to-end performance objectives,” IEEE Communications Magazine, Dec. 2006  Cognitive Radio Technologies, 2007

    48. Dynamic Frequency Selection 802.11h 802.11y 802.11 for TV bands? Distributed Collaboration 802.16h Collaborative Sensing 802.22 Radio Resource Maps 802.16h 802.11y Policy radios 802.11e 802.11j Emerging Commercial Implementations  Cognitive Radio Technologies, 2007

    49. 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, 2007