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James Neel james.neel@crtwireless.com April 25-26, 2007

Analysis and Design of Cognitive Radio Networks and Distributed Radio Resource Management Algorithms. James Neel james.neel@crtwireless.com April 25-26, 2007. CRT Information. Small business officially incorporated in Feb 2007 to commercialize cognitive radio research

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James Neel james.neel@crtwireless.com April 25-26, 2007

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  1. Analysis and Design of Cognitive Radio Networksand Distributed Radio Resource Management Algorithms James Neel james.neel@crtwireless.com April 25-26, 2007

  2. CRT Information Small business officially incorporated in Feb 2007 to commercialize cognitive radio research Email: james.neel@crtwireless.com reedjh@crtwireless.com Website: crtwireless.com Tel: 540-230-6012 Mailing Address: Cognitive Radio Technologies, LLC 147 Mill Ridge Rd, Suite 119 Lynchburg, VA 24502

  3. CRT’s Strengths Frequency Adjustments • Analysis of networked cognitive radio algorithms (game theory) • Design of low complexity, low overhead (scalable), convergent and stable cognitive radio algorithms • Infrastructure, mesh, and ad-hoc networks • DFS, TPC, AIA, beamforming, routing, topology formation Average Link Interference Net Interference Statistics from yet to be published DFS algorithm for ad-hoc networks P2P 802.11a links in 0.5kmx0.5km area (discussed in tomorrow’s slides)

  4. Selected Publications (why selected) • (First paper to propose application of game theory to cognitive radio networks) • J. Neel, R. Buehrer, J. Reed, and R. Gilles, “Game Theoretic Analysis of a Network of Cognitive Radios,” Midwest Symposium on Circuits and Systems 2002 • (First paper to use game theory to examine convergence properties of cognitive radio networks) • J. Neel, J. Reed, and R. Gilles, “Convergence of Cognitive Radio Networks,” Wireless Communications and Networking Conference 2004, March 21-25, 2004, vol. 4, pp. 2250-2255. • (Outstanding Paper Award) • J. Neel, J. Reed, R. Gilles, “Game Models for Cognitive Radio Analysis,” SDR Forum 2004 Technical Conference, Nov. 2004, paper # 01.5-05. • (Outstanding Paper Award) • J. Neel, J. Reed, R. Gilles, “The Role of Game Theory in the Analysis of Software Radio Networks,” SDR Forum Technical Conference,San Diego Nov. 11-12, 2002, paper # 04.3-001.

  5. Selected Publications (why selected) • (Textbook chapter closely related to today’s presentation) • J. Neel. J. Reed, A. MacKenzie, Cognitive Radio Network Performance Analysis in Cognitive Radio Technology, B. Fette, ed., Elsevier August 2006. • (Shorter upcoming tutorial across the pond) • J. Neel, “Game Theory in the Analysis and Design of Cognitive Radio Networks,” DySPAN07 April 17, 2007,. • (Stuff beyond Cognitive Radio Networks) • W. Tranter, J. Neel, and C. Anderson, Simulation of Ultra Wideband Communication Systems, in An Introduction to Ultra Wideband Communication Systems, Prentice Hall 2005. • J. Neel and J. Reed. Case Studies in Software Radio Design, in Jeffrey H. Reed. Software Radios: A Modern Approach to Radio Engineering, Prentice Hall 2002. • J. Reed, J. Neel and S. Sachindar, Analog to Digital and Digital to Analog Conversion, in Jeffrey H. Reed, Software Radios: A Modern Approach to Radio Engineering, Prentice Hall 2002.

  6. Tutorial Background • Most material from my three week defense • Very understanding committee • Dissertation online @ http://scholar.lib.vt.edu/theses/available/etd-12082006-141855/ • Original defense slides @ http://www.mprg.org/people/gametheory/Meetings.shtml • Other material from training short course I gave in summer 2003 • http://www.mprg.org/people/gametheory/Class.shtml • Some material is new • Consider presentation subject to existing NDAs

  7. Hypothesis: Applying game theory and game models (potential and supermodular) to the analysis of cognitive radio interactions Provides a natural method for modeling cognitive radio interactions Significantly speeds up and simplifies the analysis process (can be performed at the undergraduate level – Senior EE) Permits analysis without well defined decision processes (only the goals are needed) Can be supplemented with traditional analysis techniques Can provides valuable insights into how to design cognitive radio decision processes Has wide applicability Research in a nutshell

  8. Research in a nutshell • Focus areas: • Formalizing connection between game theory and cognitive radio • Collecting relevant game model analytic results • Filling in the gaps in the models • Model identification (potential games) • Convergence • Stability • Formalizing application methodology • Developing applications

  9. Presentation Overview • Cognitive Radio • Basic Functionality • Applications • Classifications • Modeling Cognitive Radio Behavior • Motivating Problems • Analysis Objectives • Basic Models • “Traditional” Analysis Insights • Evolution Functions • Contraction Mappings

  10. Presentation Overview • Game Theory • Normal Form games • Mixed Strategies • Extensive Form Games • Repeated Games • Special Game Models • Supermodular games • Potential games • Algorithms • Ad-hoc power control • Sensor network formation • Interference Reducing Networks • Infrastructure DFS • Ad-hoc DFS • Joint algorithms

  11. Approximate Timeline Day 1 (April 25th) Day 2 (April 26th)

  12. Cognitive Radio Concepts How does a radio come to be “cognitive”?

  13. 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)

  14. Defining Cognitive Radio is Surprisingly Difficult • [Mitola 1999] coined the term to refer to • “A radio that employs model based reasoning to achieve a specified level of competence in radio-related domains.” • [Haykin 2005] • “An intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g., transmit-power, carrier-frequency, and modulation strategy) in real-time, with two primary objectives in mind: • highly reliable communications whenever and wherever needed; • efficient utilization of the radio spectrum.

  15. Regulatory • FCC • “A radio that can change its transmitter parameters based on interaction with the environment in which it operates.” • NTIA • “A radio or system that senses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput, mitigate interference, facilitate interoperability, and access secondary markets.” • ITU (Wp8A) • “A radio or system that senses and is aware of its operational environment and can dynamically and autonomously adjust its radio operating parameters accordingly.”

  16. SDR Forum Definitions • Cognitive Radio Working Group • “A radio that has, in some sense, (1) awareness of changes in its environment and (2) in response to these changes adapts its operating characteristics in some way to improve its performance or to minimize a loss in performance.” • Cognitive Radio Special Interest Group • “An adaptive, multi-dimensionally aware, autonomous radio (system) that learns from its experiences to reason, plan, and decide future actions to meet user needs.”

  17. IEEE • IEEE USA • “A radio frequency transmitter/receiver that is designed to intelligently detect whether a particular segment of the radio spectrum is currently in use, and to jump into (and out of, as necessary) the temporarily-unused spectrum very rapidly, without interfering with the transmissions of other authorized users.” • IEEE 1900.1 • “A type of radio that can sense and autonomously reason about its environment and adapt accordingly. This radio could employ knowledge representation, automated reasoning and machine learning mechanisms in establishing, conducting, or terminating communication or networking functions with other radios. Cognitive radios can be trained to dynamically and autonomously adjust its operating parameters.”

  18. Virginia Tech Cognitive Radio Working Group • “An adaptive radio that is capable of the following:a) awareness of its environment and its own capabilities, b) goal driven autonomous operation, c) understanding or learning how its actions impact its goal, d) recalling and correlating past actions, environments, and performance.”

  19. Cognitive Radio Capability Matrix

  20. 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

  21. 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

  22. 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.

  23. 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 Adapted From Mitola, “Cognitive Radio for Flexible Mobile Multimedia Communications ”, IEEE Mobile Multimedia Conference, 1999, pp 3-10.

  24. 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

  25. Policy-Based Radio mask • A radio whose operation/ adaptations are governed by a set of rules • Almost necessarily coupled with cognitive radio • Allows flexibility for setting spectral policy to satisfy regional considerations frequency Policies

  26. Cognitive Radio Applications

  27. Strong Artificial Intelligence • Concept: Make a machine aware (conscious) of its environment and self aware (probably a good thing) • A complete failure

  28. Weak Artificial Intelligence • Concept: Develop powerful (but limited) algorithms that intelligently respond to sensory stimuli • Applications • Machine Translation • Voice Recognition • Intrusion Detection • Computer Vision • Music Composition

  29. 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 engine 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.

  30. Weak/Procedural Cognitive Radios • 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) • A function of the fuzzy definition • Implementations: • CWT Genetic Algorithm Radio • MPRG Neural Net Radio • Multi-dimensional hill climbing DoD LTS (Clancy) • Grambling Genetic Algorithm (Grambling) • Simulated Annealing/GA (Twente University) • Existing RRM Algorithms?

  31. Strong/Ontological Radios • Radio’s adaptations determined by some reasoning engine which is guided by its ontological knowledge base (which is informed by observations) • Proposed Implementations: • CR One Model based reasoning (Mitola) • Prolog reasoning engine (Kokar) • Policy reasoning (DARPA xG)

  32. 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 Intelligent Algorithms and Cognitive Engines

  33. Example Architecture from CWT Observation Orientation Action Decision Learning Models

  34. DFS in 802.16h Decision, Action Service in function Channel Availability Check on next channel Choose Different Channel • Drafts of 802.16h defined a generic DFS algorithm which implements observation, decision, action, and learning processes • Very simple implementation Observation Available? No Observation Yes In service monitoring of operating channel Decision, Action No Detection? Select and change to new available channel in a defined time with a max. transmission time Yes Stop Transmission Learning Start Channel Exclusion timer Log of Channel Availability Channel unavailable for Channel Exclusion time Yes Available? No Modified from Figure h1 IEEE 802.16h-06/010 Draft IEEE Standard for Local and metropolitan area networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Amendment for Improved Coexistence Mechanisms for License-Exempt Operation, 2006-03-29 Background In service monitoring (on non-operational channels)

  35. Explicitly opened up Japanese spectrum for 5 GHz operation Part of larger effort to force equipment to operate based on geographic region, i.e., the local policy 802.11j – Policy Based Radio 2.4 GHz 5 GHz • US • UNII Low 5.15 – 5.25 (4) 50 mW • UNII Middle 5.25 – 5.35 (4) 250 mW • UNII Upper 5.725-5.825 (4) 1 W • 5.47 – 5.725 GHz released in Nov 2003 • Europe • 5.15-5.35 200 mW • 5.47-5.725 1 W • Japan • 4.9-5.091 • 5.15-5.25 (10 mW/MHz) unlicensed

  36. 802.11e – Almost Cognitive • Enhances QoS for Voice over Wireless IP (aka Voice over WiFi ) and streaming multimedia • Anticipated changes • Enhanced Distributed Coordination Function (EDCF) • Shorter random backoffs for higher priority traffic • Hybrid coordination function (orientation) • Defines traffic classes • In contention free periods, access point controls medium access (observation) • Stations report to access info on queue size. (Distributed sensing)

  37. Dynamic Frequency Selection (DFS) Avoid radars Listens and discontinues use of a channel if a radar is present Uniform channel utilization Transmit Power Control (TPC) Interference reduction Range control Power consumption Savings Bounded by local regulatory conditions 802.11h – Unintentionally Cognitive

  38. 802.11h: A simple cognitive radio Observe • Must estimate channel characteristics (TPC) • Must measure spectrum (DFS) Orientation • Radar present? • In band with satellite?? • Bad channel? • Other WLANs? Decision • Change frequency • Change power • Nothing Action Implement decision Learn • Not in standard, but most implementations should learn the environment to address intermittent signals Decide Orient Observe Learn Act Outside World

  39. Improved Coexistence Mechanisms for License-Exempt Operation Basically, a cognitive radio standard Incorporates many of the hot topics in cognitive radio Token based negotiation Interference avoidance Network collaboration RRM databases Coexistence with non 802.16h systems Regular quiet times for other systems to transmit 802.16h From: M. Goldhamer, “Main concepts of IEEE P802.16h / D1,” Document Number: IEEE C802.16h-06/121r1, November 13-16, 2006.

  40. General Cognitive Radio Policies in 802.16h • Must detect and avoid radar and other higher priority systems • All BS synchronized to a GPS clock • All BS maintain a radio environment map (not their name) • BS form an interference community to resolve interference differences • All BS attempt to find unoccupied channels first before negotiating for free spectrum • Separation in frequency, then separation in time

  41. 802.11h (“Weak” CR on hardware radios – defined shortly) • Idea: Upgrade control processes to permit use bands 802.11a devices to operate as secondary users to radar and satellites • Dynamic Frequency Selection (DFS) • Avoid radars • Listens and discontinues use of a channel if a radar is present • Uniform channel utilization • Transmit Power Control (TPC) • Interference reduction • Range control • Power consumption Savings • Bounded by local regulatory conditions

  42. Wireless Regional Area Networks (WRAN) Aimed at bringing broadband access in rural and remote areas Takes advantage of better propagation characteristics at VHF and low-UHF Takes advantage of unused TV channels that exist in these sparsely populated areas (Opportunistic spectrum usage) 802.22 specifications TDD OFDMA PHY DFS, sectorization, TPC Policies and procedures for operation in the VHF/UHF TV Bands between 54 MHz and 862 MHz Target spectral efficiency: 3 bps/Hz Point-to-multipoint system 100 km coverage radius IEEE 802.22 – Planned Cognition • Devices • Base Station (BS) • Customer Premise Equipment (CPE) • Master/Slave relation • BS is master • CPE slave • Max Transmit CPE 4W

  43. 802.22: Cognitive Aspects • Observation • Aided by distributed sensing (subscriber units return data to base) • Digital TV: -116 dBm over a 6 MHz channel • Analog TV: -94 dBm at the peak of the NTSC (National Television System Committee) picture carrier • Wireless microphone: -107 dBm in a 200 kHz bandwidth. • Possibly aided by spectrum usage tables • Orientation • Infer type of signals that are present • Decision • Frequencies, modulations, power levels, antenna choice (omni and directional) • Policies • 4 W Effective Isotropic Radiated Power (EIRP) • Spectral masks, channel vacation times

  44. Outside World The Interaction Problem • Outside world is determined by the interaction of numerous cognitive radios • Adaptations spawn adaptations

  45. Dynamic Spectrum Access Pitfall • Suppose • g31>g21;g12>g32 ;g23>g13 • Without loss of generality • g31, g12, g23 = 1 • g21, g32, g13 = 0.5 • Infinite Loop! • 4,5,1,3,2,6,4,… 2 3 1 Interference Characterization 0 1 2 3 4 5 6 7

  46. Implications • In one out every four deployments, the example system will enter into an infinite loop • As network scales, probability of entering an infinite loop goes to 1: • 2 channels • k channels • Even for apparently simple algorithms, ensuring convergence and stability will be nontrivial

  47. Scenario: Distributed SINR maximizing power control in a single cluster For each link, it is desirable to increase transmit power in response to increased interference Steady state of network is all nodes transmitting at maximum power Locally optimal decisions that lead to globally undesirable networks Power SINR Insufficient to consider only a single link, must consider interaction

  48. 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

  49. 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

  50. NE3 NE2 a2 NE3 focus NE1 NE2 a2 (Radio 2’s available actions) NE3 NE3 NE1 a1 NE2 a2 NE2 a1 a2 NE1 NE1 (Radio 1’s available actions) a3 a1 a1 Network Analysis Objectives • Steady state characterization • Steady state optimality • Convergence • Stability/Noise • Scalability Optimality Are these outcomes desirable? Do these outcomes maximize the system target parameters? Stability/Noise How do system variations/noise impact the system? Do the steady states change with small variations/noise? Is convergence affected by system variations/noise? Convergence How do initial conditions impact the system steady state? What processes will lead to steady state conditions? How long does it take to reach the steady state? Scalability As the number of devices increases, How is the system impacted? Do previously optimal steady states remain optimal? Steady State Characterization Is it possible to predict behavior in the system? How many different outcomes are possible?

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