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Cognitive Radio Introduction & Main Issues

Cognitive Radio Introduction & Main Issues. Kuncoro Wastuwibowo IEEE Indonesia Section. Cognitive Radio. Y U NO make wireless systems computationally intelligent ??. Rationale & History. Rationale behind CR.

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Cognitive Radio Introduction & Main Issues

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  1. Cognitive RadioIntroduction & Main Issues Kuncoro Wastuwibowo IEEE Indonesia Section

  2. Cognitive Radio Y U NO make wireless systems computationally intelligent ??

  3. Rationale & History

  4. Rationale behind CR • The current policies of spectrum block result in inefficiency of spectrum usage. In some block, the spectra are saturated, whereas other bands are underused. The improvement will need a flexible yet regulated use of spectrum band. • Mobile and multi-device lifestyle currently requires multi-band and multi-platform wireless technology, which should be simplified and/or future-enhanced with software-defined wireless technology. • Context-aware service and applications could be improved with cross-layer optimization including the flexible use of spectrum.

  5. CR Ideal Objective Most-Effective (or Any Available Spectrum) Device Cognitive Radio Generic TX / RX

  6. CR-like Systems

  7. Standardisation

  8. Standards for CR Aspects

  9. IEEE Standardisation

  10. IEEE DySPAN SC Work Scope

  11. IEEE Dyspan SC Working Groups

  12. CR Aspects in IEEE 802 Standards

  13. CR Aspects in IEEE 802 Standards

  14. Functionality

  15. Definitions

  16. Adaptive Wireless Systems

  17. CR Functionality Policies, Rules, etc Decision Database Radio Environment, User Behaviour, Device State, etc TX Reconfigurable Radio Platform RX Learning & Reasoning Sensing

  18. CR in Heterogeneous Nextgen Wireless Access

  19. Heterogeneous Wireless Access Network Management Cellular Metropolitan Short-Range Network reconfiguration management WiMAX WiMAX II (806.16m) WiFi (802.11n) WiFi NG 3G 4G DSA-enabled Radios 1900.4 Terminal IEEE 1900.4 1900.4 Terminal Legacy terminal Terminal reconfiguration management Terminal reconfiguration management

  20. Spectrum Selection: Context

  21. Spectrum Selection:Decision

  22. System Architecture

  23. Functional Architecture

  24. Multiple Player Issues:Self-Coexistence

  25. Switch or Stay: Expected Cost • Assume: • N = number of networks competing • M = number of band • Two networks cannot share a band, because it will suffer the QoS • Any interfering network i in a specific band may choose to ‘stay’ or ‘switch’ • Expected cost to find a clear channel: • where • si, s-i strategy chosen by i and by other network • c  cost of single switching • f(N,M)  function that depicts the varying behavior of the cost with N and M. For example f(N,M) = NM/(M-N)

  26. Challenge Accepted

  27. Switch or Stay: Cost Function If i chooses to stay, possibly: All others will switch, creating clear band for i All others might stay, wasting the stage, and repeating the game G Some networks will switch, while the rest will stay and creating a subgame G’ The cost function is: The optimization problem in this game is to find a mechanism of switching or staying such that the cost incurred can be minimized and an equilibrium can be achieved. Assuming all the players (networks) are rational, there might be a set of strategies with the property that no network can benefit by changing its strategy unilaterally while the other networks keep their strategies unchanged (Nash equilibrium).

  28. Switch or Stay: Expected Cost Prob. If: p is the probability to switch and (1-p) is the probability of stay j is the number of other networks willing to switch Qj denotes the probability of j networks switching out of other N − 1 networks Then: the expected costs of i if it chooses to switch or to stay are

  29. Optimising To find the optimal value, both equations are equated Using binomial equations etc, For any values of N and M, p has a nonzero finite value, thus proving the existence of a mixed strategy Nash equilibrium point.

  30. Switching Cost for N=20 • Average system convergence cost with 20 competing cognitive radio (CR) networks. • With increase in number of available bands, the convergence cost decreases. • The convex nature of the curves proves that a point of minima exists for each of the curve. This minima corresponds to the Nash equilibrium strategy (p).

  31. Cost vs N/M Ratio • System convergence costs following mixed strategy space for a varying network:band ratio (50−90%) • With an increase in the network : band ratio the system convergence cost increases almost exponentially.

  32. Next Issues

  33. Future of CR: Network Radio Policy Enforcement Entity Incentive Entity Security Module Coexistence Module Topology Network Coding Cross-Layer Optimisation Network Cognitive Radio MAC + MIMO PHY

  34. Next Works to Discuss • Spectrum sensing & other DSA input • Sharing technologies • Location & context-awareness • Cognitive learning & adapting • Collaborative radio-coverage and capacity extensions • Self-configuring, optimising, healing technologies • Autonomic interoperability • Cognitive routing & prioritisation • Smart antenna management • Heterogeneous networks spectrum management • Small cells & spectrum management • Cognitive MIMO • Intersystem handoff & network resource allocation • End-to-end QoS, security, and trust system

  35. Let’s Discuss It !!

  36. Reference • FabrizioGranelli & al. Standardization and Research in Cognitive and Dynamic Spectrum Access Networks: IEEE SCC41 Efforts and Other Activities. IEEE Communications Magazine, January 2010. • Krzysztof Iniewski (ed). Convergence of Mobile and StationaryNext-Generation Networks. Wiley, 2010. • Lee Pucker. Review of Contemporary Spectrum Sensing Technologies. Report for IEEE-SA P1900.6 Standards Group • Min Song & al. Dynamic Spectrum Access: From Cognitive Radio to Network Radio. IEEE Wireless Communications, February 2012. • Paul Houze& al. IEEE 1900.4 WG: IEEE 1900.4 Standard Overview. Presentation. • R. VenkateshaPrasad & al,Cognitive Functionality in Next Generation Wireless Networks: Standardization Efforts. IEEE Communications Magazine, April 2008. • SoodeshBuljore & al. Architecture and Enablers for Optimized Radio Resource Usage in Heterogeneous Wireless Access Networks: The IEEE 1900.4 Working Group. IEEE Communications Magazine, January 2009.

  37. Kuncoro Wastuwıbowo • Telkom Indonesia • Multimedia Division • Senior Service Creation (2010-now) • IEEE • Indonesia Section • Vice Chair (2012) • Comsoc, Indonesia Chapter • Chairman (2009-2011) • Vice Chair (2007-2008) • Internetworking Indonesia Journal • Editor Contact • Mail / Gtalkkuncoro@telkom.cc • Twitter @kuncoro • Mobile +62-21-3375-8000

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