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Joint Throughput Optimization for Wireless Mesh Networks

Joint Throughput Optimization for Wireless Mesh Networks. IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 8, 2009. Xiang-Yang Li, Senior Member, IEEE, Ashraf Nusairat , Student Member, IEEE, Yanwei Wu, Student Member, IEEE, Yong Qi , Member, IEEE,

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Joint Throughput Optimization for Wireless Mesh Networks

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  1. Joint Throughput Optimizationfor Wireless Mesh Networks IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 8, 2009 Xiang-Yang Li, Senior Member, IEEE, AshrafNusairat, Student Member, IEEE,Yanwei Wu, Student Member, IEEE, Yong Qi, Member, IEEE, JiZhong Zhao, Member, IEEE,Xiaowen Chu, Member, IEEE, and Yunhao Liu, Senior Member, IEEE R97725024 戴智斌 R97725037 蔡永斌

  2. Outline • Introduction • System Model and Assumption • Problem Formulation • Efficient TDMA Scheduling • Performance Evaluation • Conclusion

  3. Introduction

  4. Introduction (1/2) • Wireless mesh network (WMNs) are being used for extending the Internet connectivity for mobile nodes. • Many US cities (e.g. Medford, Oregon, Chaska, Minnesota; Nashville, Illinois; and Gilbert, Arizona) have deployed WMNs.

  5. Introduction (2/2) • The major problem of WMNs is the reduction of capacity due to interference caused by simultaneous transmissions. • How to optimize joint throughput under certain fairness constraints via joint routing, link scheduling, and dynamic channel assignment.

  6. System Model and Assumption

  7. System Model and Assumption • Wireless Mesh Network • MMM for (multihopmultiradio multichannel) • with multiple sink nodes (wireless router with gateway function) Internet

  8. Assumptions • Different nodes may have… • Multiple radios • Multiple channels • Different transmission range and interference range • Combined channel

  9. Dynamic Channel Combining Interference Node 1 Node 2 Channel 1 Channel 1 Channel 1 Channel 2 Channel 2 Channel 2 Combining Channel 3 Channel 3 Channel 3 Channel 4 Channel 4 Channel 4 Channel 5 Channel 5 Channel 5

  10. System Model and Assumption • Multiple radios • Virtual nodes and links w u v z

  11. System Model and Assumption • Why we need interference models? • PrIM, fPrIM, RTS/CTS, TxIM Interference Sender 1 Sender 2 Receiver 2 Receiver 1

  12. Problem Formulation

  13. Problem Formulation • Given: • an MMM WMN G = (V,E), flow demand l(u) from each sourch node u. • Objective • Maximize Fairness • Maximize Joint Throughput

  14. Maximize Fairness The flow coming to the node The flow going out of the node Achieved Flow Node 2 Node 5 Mobile Client Node 3 Node 1 Node 6 Node 4 Node 7 Mobile Client

  15. MAXFairness LP

  16. Maximize Joint Throughput Internet Gateway Router Node Node Node Node Node Node Node Node Node Node Node Node Node Node

  17. MAX Joint Throughput LP

  18. Interference Free Schedule • Link Scheduling • Give each link a schedule • list of time slots and corresponding channels • Objective • interference free

  19. Mixed IP MAX Flow Fairness

  20. LP Flow Fairness • Maximize Fairness

  21. Maximize Joint Throughput

  22. Efficient TDMA Scheduling

  23. Efficient TDMA Scheduling • Centralized scheduling for link transmission • Assume that T is the number of time slots per scheduling period. • We need to schedule time slots for a virtual link using channel .

  24. Efficient TDMA Scheduling

  25. Links Sorting • Different links sorting algorithm • Our algorithm relies on some special sorting of the links, which depends on the interference models. • No common sorting that works for all interference models.

  26. Improvement • Parametric searching improve the overall achieved flow.

  27. Performance Evaluation

  28. Impact of Multichannels (1/1) When channel combining is performed, it provides higher throughput and higher fairness.. Increasing the number of channels per radio increases the throughput and fairness.

  29. Impact of Multiradios (1/1) When channel combining is performed, it provides higher throughput and higher fairness.. The bouncing is that the actual number of radios assigned to each node is randomly generated for each simulation run. Increasing the number of radios per node “seems to” increase the throughput and fairness.

  30. Impact of Interference Model (1/1) With channel combining, the network receives the highest fairness and throughput under PrIM, while it receives the lowest fairness under the RTS/CTS model and lowest throughput throughput under TxIM.

  31. Impact of Interference Model (1/1) Without channel combining, the network receives the highest fairness and throughput under PrIM, while it receives the lowest fairness and throughput under the RTS/CTS model.

  32. Conclusion

  33. Conclusion (1/1) • The main contributions of this paper are • Theoretical performance guarantee for algorithms. • Impact of channel combining. • Realistic models and other restrictions.

  34. Q & A Thank You !

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