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Modeling VoIP in Cognitive Radio Network

Modeling VoIP in Cognitive Radio Network. Students: Taly Sessler 038741401 Ben Rubovitch 065631475 Instructor: Boris Oklander Semester: Winter 2010. Index. Introduction 1.1 VoIP 1.2 Cognitive Radio 2. Project’s Goals 3. Model Description 4. Simulation design 5. Results

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Modeling VoIP in Cognitive Radio Network

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  1. Modeling VoIP in Cognitive Radio Network Students: Taly Sessler 038741401 Ben Rubovitch 065631475 Instructor: Boris Oklander Semester: Winter 2010

  2. Index • Introduction 1.1 VoIP 1.2 Cognitive Radio 2. Project’s Goals 3. Model Description 4. Simulation design 5. Results • Conclusions • Summery

  3. Introduction to VoIP • Voice over Internet Protocol- VoIP is a general term for a family of transmission technologies for delivery of voice communications over IP networks such as the Internet or other packet-switched networks. • Flexibility - VoIP can facilitate tasks and provide services that may be more difficult to implement using the PSTN. • Many telephone calls over a single channel. • Secure calls using standardized protocols. • Location independence. • Integration with other Internet services.

  4. Introduction to VoIPRelevant challenges • Quality of Service – the network cannot ensure that the data packets are delivered in sequential order, or provide Quality of Service (QoS) guarantees, VoIP implementations may face problems mitigating latency and jitter. • Delay Jitter - in the context of computer networks, the term jitter is often used as a measure of the variability over time of the packet latency across a network. • Jitter Buffer - Some systems use sophisticated delay-optimal de-jitter buffers that are capable of adapting the buffering delay to changing network jitter characteristics.

  5. Introduction to VoIP • clustering / dispersion → overflow / time-outs • Trade-offs management: Delay ↔ Loss

  6. Introduction to Cognitive Radio • Cognitive Radio (CR) is a new wireless communication paradigm. • CR characteristics: • Based on Software Defined Radio (SDR). • CR is aware of its environment and use case. • Spectrum Sensing • Spectrum Analysis • Spectrum Decision

  7. Project Goals • Studying VoIP technology with emphasis on Oos aspects • Implementation of VoIP CRNModel usingMATLAB@ • Executing and Performance studying

  8. Network & Codec Jitter Buffer Voice Quality Loss Delay λ K TDelay jitter buffer jitter buffer codec codec codec codec System’s Model E-model R-Factor MOS • Network conditions • Delay • Loss • Codec characteristics • Equipment impairment • Loss robustness jitter buffer controller

  9. Network & Codec Jitter Buffer Voice Quality jitter buffer jitter buffer codec codec codec codec CRN System’s Model E-model R-Factor MOS jitter buffer controller CRN

  10. Network & Codec Jitter Buffer Voice Quality Loss Delay λ K TDelay jitter buffer jitter buffer codec codec codec codec CRN System’s Model E-model R-Factor MOS • Network conditions • Delay • Loss • Codec characteristics • Equipment impairment • Loss robustness C(t) jitter buffer controller

  11. Spectrum opportunities

  12. Network Simulator Scenarios generator Network simulator Adaptive Jitter Buffer MOS Channel State Simulator Performance Studying

  13. Channel State Simulator Design Inputs: M – number of channels Ci(t) – state of ith channel i=1,2,…,M PU parameters α,β Simulation time Outputs: Channels(t) – state of channels

  14. Channel State Simulator Design Change 1: for i = 1:Network.channel_set.M nst = find(Network.channels(i).times > slot(n),1)-1; network_state = network_state + Network.channels(i).state(nst); if isempty(network_state) error('1'); end End Change 2: if network_state == 0 Stream.TOA(id) = -1; else Stream.TOA(id) = Stream.TOC(id)+Session.T_packet*50/network_state; if Stream.TOA(id) < Stream.TOA(id-1) Stream.TOA(id) = Stream.TOA(id-1)+ Session.T_packet/10000; end end

  15. Design Description using UML tools

  16. Class Diagrams

  17. Results

  18. Results and Conclusions Jitter Buffer Algorithm Types Analytic - AJB AR-1 – re-evaluation AR-N – re-evaluation Constant Delay

  19. C=0.1

  20. C=2

  21. C=5

  22. C=10

  23. C=5, K=0.01, Network type=1

  24. C=50, K=10-150

  25. Conclusions • We can see that for each situation there is an algorithm that fits it, but there is no good algorithm for all Network and Participant types. • The use of Algorithm will be done by the state of known factors in the Network and Participant with the use of the tables above

  26. Summery • In this project we integrated a network simulation that fits better with realistic Network. • Upgraded the Jitter Buffer’s algorithm by dumping packets that came later then their successors and simulated a more realistic Time Of Arrival. • Generated a table that covers a vast variety of situations which the Jitter Buffer can choose an algorithm from

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