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.

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Presentation transcript:

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 Impact of Self-Similar to CSMA/CA performance Impact of Self-Similar to CSMA/CA performance with RTS/CTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 Impact of Self-Similar to CSMA/CA performance with RTS/CTS (1/4)

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

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

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

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

Thanks for your attendance

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

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