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1 Multi-Rate Adaptation with Interference and Congestion Awareness IPCCC 2011 University of California, Santa Cruz* Huawei Innovation Center^ 11/17/2011.

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Presentation on theme: "1 Multi-Rate Adaptation with Interference and Congestion Awareness IPCCC 2011 University of California, Santa Cruz* Huawei Innovation Center^ 11/17/2011."— Presentation transcript:

1 1 Multi-Rate Adaptation with Interference and Congestion Awareness IPCCC 2011 University of California, Santa Cruz* Huawei Innovation Center^ 11/17/2011 Duy Nguyen*, J.J. Garcia-Luna-Aceves* and Cedric Westphal*^

2 2 Rate/Link Adaptation

3 3 Challenges SR Limited Feedback N1N2 Interference

4 4 Challenges SR Limited Feedback N1N2 Interference Path Attenuation Multi-path Fading

5 5 Rate Adaptation: Explicit vs Implicit Approach Explicit: –Receiver-driven: dictates what rate that should be used –CSI S/N measurements & BER estimate Implicit: –Sender-driven: Inferring the channel condition on the receiver –Based on RSSI measurements and ACK Packets

6 6 Rate Adaptation: Explicit vs Implicit Approach Explicit: –Pros: measurements estimate from PHY –Cons: Incurs additional overhead, possible stale feedback due to short channel coherence time. Implicit: –Pros: simplicity –Cons: must infer the channel condition on the receiver side

7 7 Implicit Approach can be very effective Inspired by AIMD Scheme Credit-based systems, using both packet window and time window Allows progressive rate increase and immediate rate decrease Reactive to changes in the environment Compatible with current WiFi Systems Multi-rate Adaptation with Interference Congestion Awareness (MAICA)

8 8

9 9 Credit Bucket Packets Bucket & Time Window

10 10 Multi-rate Adaptation with Interference Congestion Awareness (MAICA) Credit Bucket Packets Bucket & Time Window Acceptable Threshold Awarding a credit

11 11 Multi-rate Adaptation with Interference Congestion Awareness (MAICA) Credit Bucket Packets Bucket & Time Window Acceptable Threshold Awarding a credit

12 12 Multi-rate Adaptation with Interference Congestion Awareness (MAICA) Credit Bucket Packets Bucket & Time Window Acceptable Threshold Credit has been reached Increase Rate

13 13 Multi-rate Adaptation with Interference Congestion Awareness (MAICA) Credit Bucket Packets Bucket & Time Window Acceptable Threshold Decrease Rate

14 14 Multi-rate Adaptation with Interference Congestion Awareness (MAICA) Credit Bucket Packets Bucket & Time Window Acceptable Threshold Decrease Rate

15 15 Multi-rate Adaptation with Interference Congestion Awareness (MAICA) Credit Bucket Packets Bucket & Time Window More errors than success packets Decrease Rate Multiplicatively

16 16 Multi-rate Adaptation with Interference Congestion Awareness (MAICA) Credit Bucket Packets Bucket & Time Window Transmitting at a lower rate

17 17 MAICA MARKOV MODEL

18 18 ARF and MAICA’s Markov Models

19 19 NS 3 Simulation Setup Transmission Range140m Packet Size512 bytes Droptail Queue Length100 PHYIEEE a Implicit rate adaptation evaluations Compare against other current well-known rate adaptations such as AMRR, CARA, RRAA Ported the popular Linux Minstrel rate adaptation to ns-3 simulations MAICA consistently performs well in all scenarios

20 20 Fading with Movement SR

21 21 Scenario Setup Exponential distributed flows with mean of 3s 20m distance between each node

22 22 Propagation Loss Models

23 23 50 Flows in 500mx500m Topology with Random Node Placement

24 24 30 Flows and 2D Mobility in 500mx500m Topology with Random Node Placement

25 25 Fairness Evaluation Jain’s Fairness Evaluate Jain’s Fairness Index and Aggregate Throughput MAICA achieved fairness not at the expense of performance

26 26 Fairness with 16 Flows Static Grid Aggregate Throughput 8% gain over CARA

27 27 Fairness with 100 Flows Static Grid Aggregate Throughput 25% gain over CARA in dense networks

28 28 Linux Implementation of MAICA ImplementationMadwifi and Ath5k ChipsetsAR5212 and AR5213 Linux Version SetupDestination Node: plugged in Source Node: Roam around different Access Points

29 29 Real World Experiments

30 30 Conclusion Simple and practical Inspired by TCP and AIMD Consistently performs well in various fading scenarios, especially in multi-user environment Fairness achieved not at the expense of performance Seamless integration with current Wi-Fi with Linux Kernel implementation


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