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報 告 者：林 文 祺 指導教授：柯 開 維 博士

無線區域網路中自我相似交通流量之 成因與效能評估 The origin and performance impact of self-similar traffic for wireless local area networks. 報 告 者：林 文 祺 指導教授：柯 開 維 博士. Outline. Background of Self-Similarity Properties of WLAN Traffic Estimation of Self-Similar Traffic The Origin of Self-Similarity in WLAN

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報 告 者：林 文 祺 指導教授：柯 開 維 博士

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  1. 無線區域網路中自我相似交通流量之成因與效能評估The origin and performance impact of self-similar traffic for wireless local area networks 報 告 者：林 文 祺 指導教授：柯 開 維 博士

  2. Outline • Background of Self-Similarity • Properties of WLAN Traffic • Estimation of Self-Similar Traffic • The Origin of Self-Similarity in WLAN • Impact of Self-Similar to CSMA/CA performance • Impact of Self-Similar to CSMA/CA performance with RTS/CTS

  3. Background of Self-Similarity(1/8) • Self-Similarity and Fractal

  4. Background of Self-Similarity(2/8) • Statistics of Self-Similarity Definition of Self-Similar Stochastic Process: H: Hurst parameter or self-similarity parameter

  5. Background of Self-Similarity(3/8) • Self-Similarity of Statistics Definition of Self-Similar Stochastic Sequence: Ex.

  6. Background of Self-Similarity(4/8) • Properties of Self-Similarity • Long range dependence • Slowly decaying variance • Heavy-tailed distribution

  7. Background of Self-Similarity(5/8) • Self-Similar Traffic

  8. Background of Self-Similarity(6/8) Pareto Distribution: X(t) is a Pareto distribution random process with shape parameterαand location parameter k.

  9. Background of Self-Similarity(7/8) • Variance-time Plot

  10. Background of Self-Similarity(8/8) • R/S Plot

  11. Properties of WLAN Traffic(1/2) Basic: 1 μS Aggregation: 1, 0.1, 0.01 Sec Environment: 7NB • WLAN traffic Time Unit=1 Sec Time Unit=0.1 Sec Time Unit=0.01 Sec

  12. Properties of Real Network(2/2) • Poisson traffic Time Unit=1 Sec Time Unit=0.1 Sec Time Unit=0.01 Sec

  13. Estimation of Self-Similar Traffic(1/2) • Packets Sequence on WLAN

  14. Estimation of Self-Similar Traffic(2/2) • Variance Plot & R/S Plot

  15. The Origin of Self-Similar Traffic (1/3) • Single Source without CSMA/CA

  16. The Origin of Self-Similar Traffic(2/3) • Variance Plot & R/S Plot

  17. The Origin of Self-Similar Traffic(3/3) • Variance Plot & R/S Plot for WLAN based on single Poisson Traffic. (Simulated)

  18. Impact of Self-Similar to CSMA/CA performance(1/7) • Maximum throughput • The influence of nodes on Self-Similar Traffic and Poisson Traffic • The influence of packet length on Self-Similar Traffic and Poisson Traffic

  19. Impact of Self-Similar to CSMA/CA performance(2/7) • Maximum throughput

  20. Impact of Self-Similar to CSMA/CA performance(3/7) • Maximum throughput

  21. Impact of Self-Similar to CSMA/CA performance(4/7) • The influence of nodes on Self-Similar Traffic and Poisson Traffic

  22. Impact of Self-Similar to CSMA/CA performance(5/7) • The influence of nodes on Self-Similar Traffic and Poisson Traffic

  23. Impact of Self-Similar to CSMA/CA performance(6/7) • The influence of packet length on Self-Similar Traffic and Poisson Traffic

  24. Impact of Self-Similar to CSMA/CA performance(7/7) • The influence of packet length on Self-Similar Traffic and Poission Traffic

  25. Impact of Self-Similar to CSMA/CA performance with RTS/CTS (1/4) • Maximum throughput • The influence of nodes on Self-Similar Traffic and Poisson Traffic • The influence of packet length on Self-Similar Traffic and Poisson Traffic

  26. Impact of Self-Similar to CSMA/CA performance with RTS/CTS (2/4) • Maximum throughput

  27. Impact of Self-Similar to CSMA/CA performance with RTS/CTS (3/4) • The influence of nodes on Self-Similar Traffic and Poisson Traffic

  28. Impact of Self-Similar to CSMA/CA performance with RTS/CTS (4/4) • The influence of packet length on Self-Similar Traffic and Poisson Traffic

  29. Conclusion • WLAN Traffic is Self-Similar regular & Single) • WLAN Throughput at node=5  Max • WLAN Throughput at node<5  Poisson>SS • WLAN Throughput at node>5  Poisson<SS • Impact of Packet Length • RTS/CTS not influence the characteristic of Poisson and Self-Similarity

  30. Thanks for your attendance

  31. Impact of Self-Similar to CSMA/CA performance • The Number of Nodes increment form 1 to 5

  32. Impact of Self-Similar to CSMA/CA performance • The Number of Nodes increment form 1 to 5

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