Robust Rate Adaptation in 802.11 networks Starsky H.Y.

Robust Rate Adaptation in 802.11 networks Starsky H.Y. Wong, Hao Yang, Songwu Lu and Vaduvur Bharghavan UCLA WiNG Research Group and Meru Networks IEEE 802.11 Rate Adaptation The 802.11 a/b/g/n standards allow the use of multiple transmission rates 802.11b, 4 rate options (1,2,5.5,11Mbps) 802.11a, 8 rate options (6,9,12,18,24,36,48,54 Mbps) 802.11g, 12 rate options (11a set + 11b set) The method to select the transmission rate in real

time is called Rate Adaptation Rate adaptation is important yet unspecified by the 802.11 standards 2 Rate Adaptation Example 54Mbps 12Mbps Signal is good Signal becomes weaker Sender Receiver Ideally, the transmission rate should be adjusted according to the channel condition 3

Importance of Rate Adaptation Rate adaptation plays a critical role in the throughput performance Rate too high loss ratio increases throughput decreases Rate too low under-utilize the capacity throughput decreases 4 Design Challenge Wireless channel exhibits rich channel dynamics in practical scenarios

Random channel error Mobility-induced change Collisions induced by Hidden-terminals Multiple contending clients 5 Related Work Not compliant with the 802.11 standard RBAR, OAR, etc. Needs to change the standard specification

Standard compliant ARF, AARF, SampleRate, Onoe, AMRR, CARA Cannot handle all channel dynamics Performance degradation in many cases 6 Goals and Contributions Goals Improve throughput performance Robust against various dynamics

802.11 standard compliant, easy to implement Contributions: Identify limitations of five design guidelines for existing solutions Design, implement and evaluate the Robust Rate Adaptation Algorithm (RRAA) 7 Outline Experimental Methodology Critique on current design guidelines Design of RRAA Implementation and Evaluation Ongoing work 8

Experimental Methodology Evaluation platform Programmable AP Real-time tracing and feedback, per-frame control functionalities, etc Experimental studies Controlled experiments Field trials 9 Design guidelines in Existing

Rate Adaptation Most designs follow a few conceptually intuitive and seemingly effective guidelines Decrease rate upon severe loss Use consecutive success/loss patterns Use probe packets Use PHY-layer metrics Use long-term statistics How well do they work in practice? 10 Guideline #1: Decrease transmission rate upon severe packet loss

A sender should switch to lower rates when it faces severe loss hidden-station case? Hidden Station UDP Goodput (Mbps) Receiver Sender ARF AARF SampleRate

Fixed Rate 0.65 0.56 0.58 1.46 It performs worse with Rate Adaptation! 11 Guideline #1: Decrease transmission rate upon severe packet loss The sender should not decrease the rate upon collision losses Decreasing rate increases collisions !

Decrease tx rate Severe loss Increase tx time Increase collision prob. 12 Guideline #2: Use consecutive patterns to increase/decrease rate Case 1: 10 consecutive successes increase rate Experiments: ARF, AARF

The probability of a success transmission followed by 10 consecutive successes is only 28.5% Result: The rule has 71.5% chance to fail! 13 Guideline #2: Use consecutive patterns to increase/decrease rate (contd) Case 2: 2 consecutive failures decrease rate Experiments: ARF, AARF The probability of a failure transmission followed by 2

consecutive failures is only 36.8% Result: The rule has 63.2% chance to fail! 14 Design guidelines (contd) Other guidelines: #3: Use probe packets to assess possible new rates #4: Use PHY metrics like SNR to infer new transmission rate #5: Long-term smoothened operation produces the best average performance All

suffer from problems in practice! 15 RRAA Design Short-term statistics to handle random loss mobility Adaptive RTS to handle collision 16

Short-term Statistics based Rate Adaptation Short-term statistics: Loss ratio over estimation window (20~100ms) Channel coherence time Exploit short-term opportunistic gain Threshold-based rate change: if loss ratio > PMTL rate decrease if loss ratio < PORI rate increase

Indication of bad channel quality Indication of good channel quality Otherwise, retain the current rate and continue sliding window 17 Example For 9Mbps ewnd = 10, PMTL = 39.32%, PORI = 14.34% s 1. Robust to random channel 1 failures,loss loss ratio = 10%

s s s s s s s X s s - No consecutive pattern Increase rate s s s s 4 failures, loss ratio = 40% 2.XResponsive s X X s s tos mobility s X decrease rate - Short estimation window X X s s s

s s s X 3 failures, loss ratio = 30% Rate unchanged 18 Critical Loss Ratio (P*) For any rate R, let the next lower rate be R_ and the next higher rate be R+. With a loss ratio of P*, the throughput at R becomes the same as the loss-free throughput at R_. 19

Decrease/Increase Threshold (PMTL and PORI) We set PMTL = P*(R), 1 Decrease the rate when expected throughput is less than that of loss-free (or slight loss) R_ = 1.25 in our experiments PORI = PMTL(R+) / 2 The loss ratio at the current rate R has to be small enough such that the rate increase not quickly jump back to R 20

Adaptive RTS (A-RTS) Use RTS to handle collision Tradeoff between overhead and benefits of RTS Infer collision level Packet loss without RTS Possibly due to collisions Additively increase # of packets sent with RTS Packet loss with RTS, or, success without RTS Most likely no collisions Exponentially decrease # of packets sent with RTS 21

A-RTS Example Collision may occur No collision More packets are sent with RTS if the collision level is high 22 Putting two pieces together Issue #1: What if RTS frame is lost ? Should RTS loss be counted in statistics ? Answer: NO

RTS loss most likely due to collision Issue #2: How to detect RTS loss ? Hardware does not provide such information Solution: Use the difference between RTS and DATA transmission times to infer 23 Implementation We implement RRAA on programmable AP using Agere chipset Software MAC in embedded OS

Calculating loss ratio in runtime No floating point calculation Translate loss ratio thresholds to packet loss counts and pre-load them into the AP 24 Evaluation Controlled experiments Field Trials over Channel 6 (11b)

Midnight over clear channels on 11a/g Share with UCLAWLAN 4pm - 10pm weekdays in campus buildings Compare with 3 current algorithms ARF : first published rate adaptation algorithm AARF : a stabilized version of ARF SampleRate : algorithm in Linux driver 25 Results for Controlled Experiments Static client case: Throughput gains 0.3% ~ 67.4%

Mobile client case Throughput gains 10.0% ~ 27.6% Hidden-station case Throughput gains 74% ~ 101% RRAA can infer different loss reasons and react correctly 26 Intentionally select Channel 6 for 6 hours

Static client 7~11 other APs 77~151 other clients People walking around Improve 3.8% ~ 15.3% Mobile client Improve 35.6% ~ 143.7% T C P T h ro u g h p u t (M b p s )

T C P T h ro u g h p u t (M b p s ) Results for Field Trials 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 3 2.5 2 1.5 1 0.5 0 27

Summary Critique All have problems in practice Design on five existing design guidelines RRAA Short-term statistics Adaptive RTS RRAA outperforms other rate adaptation designs in all cases 28 Ongoing work

A refined design Use both frame statistics and PHY metrics to infer collision losses zero communication overhead ! Techniques to handle clients with light traffic Techniques to work with power control Techniques to work with carrier sensing tuning Used in latest Meru product (AP 200) releases, performs better than Cisco (Aironet 1200) AP by 32%~212% 29

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