報 告 者：林 文 祺 指導教授：柯 開 維 博士

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