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TEAM 2 : 802.11 WLAN MAC layer modeling Mentor: Radu V. Balan 802.11 WLAN MAC Layer Modeling Lisa DriskellYejun Gong Xueying Hu Purdue UniversityMichigan.

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Presentation on theme: "TEAM 2 : 802.11 WLAN MAC layer modeling Mentor: Radu V. Balan 802.11 WLAN MAC Layer Modeling Lisa DriskellYejun Gong Xueying Hu Purdue UniversityMichigan."— Presentation transcript:

1 TEAM 2 : 802.11 WLAN MAC layer modeling Mentor: Radu V. Balan 802.11 WLAN MAC Layer Modeling Lisa DriskellYejun Gong Xueying Hu Purdue UniversityMichigan Technological University University of Michigan Rashi JainMechie Nkengla New Jersey Institute of Technology University of Illinois-Chicago August 17, 2007

2 TEAM 2 : 802.11 WLAN MAC layer modeling Outline  Objective  Overview  ns-2  Results (.nam)  Results (.tr)  Conclusions  Future Objectives

3 TEAM 2 : 802.11 WLAN MAC layer modeling 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. Objective

4 TEAM 2 : 802.11 WLAN MAC layer modeling Overview MAC Packets Generated IFQ Agent/UDP Transmission Agent/Null Final Destination System Layout

5 TEAM 2 : 802.11 WLAN MAC layer modeling p Overview

6 TEAM 2 : 802.11 WLAN MAC layer modeling Prob (packet dropped due to collision) = Avg # of retries = Exact # retries 012…b…L Probability1-pp(1-p)p 2 (1-p) … p b (1-p) … p L (1-p) + p L p Previous Analysis of Markov Model Overview

7 TEAM 2 : 802.11 WLAN MAC layer modeling 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. Outline Overview

8 TEAM 2 : 802.11 WLAN MAC layer modeling Ns-2 Ns-2 simulator Ns-2 Simulator Input.tcl file.tr file Output Trace File.nam file

9 TEAM 2 : 802.11 WLAN MAC layer modeling 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 TEAM 2 : 802.11 WLAN MAC layer modeling Ns-2 Example of a ns2 simulation

11 TEAM 2 : 802.11 WLAN MAC layer modeling Nam Trace Nam Trace Format -t -s -d -p -e -c -a -i -k typetspik h, r, d, +, - 0~ simTime 0~ NSTA cbr, ACK 0~ #pkts AGT, MAC

12 TEAM 2 : 802.11 WLAN MAC layer modeling Nam Trace : Medium Busy Time 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. TxFrame 1 TxFrame 2 Busy Collision

13 TEAM 2 : 802.11 WLAN MAC layer modeling Nam Trace : Medium Busy Time Medium Busy Time ↑, if NSTA ↑ < critical number Medium Busy Time ↓, if NSTA ↑ > critical number

14 TEAM 2 : 802.11 WLAN MAC layer modeling Numerical: Theoretical: But how to find p?

15 TEAM 2 : 802.11 WLAN MAC layer modeling pktSend= pktDropDueCol+ pktDropDueQueue+ pktDropDueEnd+ pktTransSuc 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’

16 TEAM 2 : 802.11 WLAN MAC layer modeling Results (.nam) Average number of retries

17 TEAM 2 : 802.11 WLAN MAC layer modeling Results (.nam) Average number of retries

18 TEAM 2 : 802.11 WLAN MAC layer modeling Example of a.tr output

19 TEAM 2 : 802.11 WLAN MAC layer modeling Results (.tr) Average number of retries From simulation From calculation

20 TEAM 2 : 802.11 WLAN MAC layer modeling Results (.tr) Average number of retries for AP with varying packet period and offset

21 TEAM 2 : 802.11 WLAN MAC layer modeling Average number of retries with varying packet period and offset 5 Stations

22 TEAM 2 : 802.11 WLAN MAC layer modeling Results (.tr) Comparison between of the average number of retries from calculation and simulation

23 TEAM 2 : 802.11 WLAN MAC layer modeling Conclusions Estimated parameter ‘p’ Determined the saturation value Established validity of the Markov model Established importance of assumption of synchronicity

24 TEAM 2 : 802.11 WLAN MAC layer modeling Future Objectives 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 (Someone Else’s) Future Objectives

25 TEAM 2 : 802.11 WLAN MAC layer modeling Questions Questions?


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