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Mentor: Radu V. Balan

Lisa Driskell Yejun Gong Xueying Hu . Purdue University. Michigan Technological University. University of Michigan. Rashi Jain Mechie Nkengla. New Jersey Institute of Technology. University of Illinois-Chicago. August 17, 2007. 802.11 WLAN MAC Layer Modeling. Mentor: Radu V. Balan.

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Mentor: Radu V. Balan

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  1. Lisa Driskell Yejun Gong Xueying Hu Purdue University Michigan Technological University University of Michigan Rashi Jain Mechie Nkengla New Jersey Institute of Technology University of Illinois-Chicago August 17, 2007 802.11 WLAN MAC Layer Modeling Mentor: Radu V. Balan

  2. Outline • Objective • Overview • ns-2 • Results (.nam) • Results (.tr) • Conclusions • Future Objectives

  3. Objective Project Objective: Analyze the relationships of the parameters for a modified EO Markov model and validate the model under certain assumptions with (ns2) network simulations.

  4. Overview System Layout Packets Generated Agent/Null Final Destination Agent/UDP Transmission IFQ MAC MAC

  5. Overview p

  6. Overview Prob(packet dropped due to collision) = Avg # of retries = Previous Analysis of Markov Model

  7. Overview Outline • Analyzed Markov model • Compared analytical results with computed results to verify the analysis. • Use analytical results compared with network simulations to determine whether the system can indeed be modeled with a Markov chain.

  8. Ns-2 .tr file Output Trace File .nam file Ns-2 Simulator Input .tcl file Ns-2 simulator

  9. Ns-2 Ns-2 simulator • Closely relates to real world. • A packet-based event-driven simulation. • Can incorporate the wireless mechanism • Allows for mobile stations • Provides animations

  10. Ns-2 Example of a ns2 simulation

  11. Nam Trace Nam Trace Format <type> -t <time> -s <source id> -d <destination id> -p <pkt-type> -e <extent> -c <conv> -a <packet attribute> -i <id> -k <trace level>

  12. Nam Trace : Medium Busy Time TxFrame 2 TxFrame 1 Collision Busy The packet is on the air from t, if at t type=‘h’ AND k=‘MAC‘ (Hop) The medium is busy in [t, t+pktTransTime] The medium is busy in collision case.

  13. Nam Trace : Medium Busy Time Medium Busy Time ↑, if NSTA ↑ < critical number Medium Busy Time ↓, if NSTA ↑ > critical number

  14. Numerical: Theoretical: But how to find p?

  15. Type=‘h’ AND k=‘AGT’ AND p=‘cbr’ Type=‘d’ AND k=‘IFQ’ AND p=‘cbr’ LastHopTime+pktTransTime>sucTime Type=‘r’ AND k=‘AGT’ AND p=‘ACK’ pktSend= pktDropDueCol+ pktDropDueQueue+ pktDropDueEnd+ pktTransSuc

  16. Results (.nam) Average number of retries

  17. Results (.nam) Average number of retries

  18. Example of a .tr output

  19. Results (.tr) Average number of retries From simulation From calculation

  20. Results (.tr) Average number of retries for AP with varying packet period and offset

  21. Average number of retries with varying packet period and offset 5 Stations

  22. Results (.tr) Comparison between of the average number of retries from calculation and simulation

  23. Conclusions Conclusions • Estimated parameter ‘p’ • Determined the saturation value • Established validity of the Markov model • Established importance of assumption of synchronicity

  24. Future Objectives (Someone Else’s)FutureObjectives • Use the .nam and .tr files to extract information about • Average service time • Average wait time • Use the above times to find relationships between parameters • Compare computed results and simulated results for different packet arrival configurations • Compute the throughput/delay • Create a deterministic model

  25. Questions Questions?

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