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A Framework for Adaptive Voice Communications Over Wireless Channels

A Framework for Adaptive Voice Communications Over Wireless Channels. Sandeep K. S. Gupta and Suhaib A. Obeidat. Outline. Problem Statement Motivation and approach Results and discussion Conclusions. Motivation. Voice is the most natural way for human comm.

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A Framework for Adaptive Voice Communications Over Wireless Channels

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  1. A Framework for Adaptive Voice Communications Over Wireless Channels Sandeep K. S. Gupta and Suhaib A. Obeidat

  2. Outline • Problem Statement • Motivation and approach • Results and discussion • Conclusions

  3. Motivation • Voice is the most natural way for human comm. • Taking advantage of silence periods. • Varying error channel conditions of a wireless link • Solution: • Changing the modulation scheme. • Changing the voice coding rate.

  4. Motivation-Cont SNR vs. BER for several modulation schemes [4].

  5. Motivation-Cont • Good Channel Condition: compressed voice at a rate of 16 kbps, denser modulation (QAM16). • Bad Channel Condition: uncompressed voice (64 kbps), and BPSK.

  6. Motivation-Cont NS That can be accommodated when using adaptive modulation Link capacity: 256ksymbol

  7. Motivation-Cont NS That can be accommodated when using adaptive encoding Link capacity: 256kbps

  8. Goal Measuring the performance of adaptive voice over a wireless connection and proposing a methodology of adaptation.

  9. QoS requirements of voice • Delay • Propagation delay (negligible) • Queuing delay • Losses • Channel Losses • Buffer Losses

  10. Current Work • Shenker compared strict versus adaptive applications. • Rate-adaptive reacts better to network congestion than other classes of adaptation (e.g., delay-adaptive) • Meo studied rate-adaptive voice comm. over IP networks • Supporting more voice communications. • Adaptive modulation: reacting to channel conditions by changing the modulation scheme and the symbol rate • Motivated newer wireless devices to support different modulation schemes.

  11. Framework

  12. Src1 Dest1 64 Kbps 64 Kbps Src2 Dest2 Src3 1.544 Mbps Dest3 Mux Module DeMux Module . . . . . . SrcN DestN Source Configuration T1 link

  13. Brady 2-state Markov Model On-off times for silence and speech Exponential dist. for speech and silence states. Speech activity 35.1% 352 ms on, 650 ms off Voice Traffic Model

  14. Elliot-Gilbert Model Represents a Good (G) and Bad (B) states. G: 16 kbps, QAM16 B: 64 kbps, BPSK Pe(G) = 10-6 Pe(B) = 10-2 4s in B, 10s in G. Wireless Channel Model

  15. Packet Loss Ratio for Adaptive vs. Non-adaptive Modulation Packet Loss Ratio =

  16. Loss Components for Adaptive vs. Non-adaptive Modulation Buffer Loss Ratio = Channel Loss Ratio =

  17. Packet Loss Ratio for Adaptive vs. Non-adaptive Encoding Packet Loss Ratio =

  18. Loss components for Adaptive vs. Non-adaptive Encoding

  19. Ratio of Packets Delayed (80-ms Threshold) for Adaptive vs. Non-adaptive Modulation Delayed Packets Ratio =

  20. DVQ for Adaptive vs. Non-adaptive Modulation + Encoding Degradation of Voice Quality =

  21. Future Work-Analytic Model • More generic • Get more confidence. • Can be used to quantify error control effect • Can be used in any analysis involving Rayleigh channel and/or adaptive modulation.

  22. Adaptive voice allows for greater flexibility and more savings Can support more voice communications. Trading quality for monetary Conclusions

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