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 Number of Sources (Bad State) Number of Sources (Good State) Total Supported 404 3811 21618 12425 032 NS That can be accommodated when using adaptive modulation Link capacity: 256ksymbol

7. Motivation-Cont Number of Sources (Bad State) Number of Sources (Good State) Total Supported 404 347 2810 11213 016 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. o 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 o Supporting more voice communications.  Adaptive modulation: reacting to channel conditions by changing the modulation scheme and the symbol rate o Motivated newer wireless devices to support different modulation schemes.

11. Framework

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

13. Voice Traffic Model  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

14. Wireless Channel Model  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.

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