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Throughput Enhancement in Wireless LANs via Loss Differentiation

Throughput Enhancement in Wireless LANs via Loss Differentiation. Michael Krishnan, Avideh Zakhor Department of Electrical Engineering and Computer Sciences U.C. Berkeley September 9, 2009. Overview. Background Type of loss in wireless networks Estimating collision probabilities

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Throughput Enhancement in Wireless LANs via Loss Differentiation

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  1. Throughput Enhancement in Wireless LANs via Loss Differentiation Michael Krishnan, Avideh Zakhor Department of Electrical Engineering and Computer Sciences U.C. Berkeley September 9, 2009

  2. Overview • Background • Type of loss in wireless networks • Estimating collision probabilities • Using estimates to improve throughput • Modulation rate adaptation • Packet length adaptation • Future Work • Participants • Dr. Wei Song • Colby Boyer • Miklos Christine • Sherman Ng

  3. Motivation & Goal • WLAN extremely easy to set up, but: • MAC layer inefficient • Link adaptation not optimal • Spatial reuse of Access Points (APs) not well understood • Throughput suffers: • Physical layer bit rate: up to 54 Mbps • Actual throughput in practice: 10-12 Mbps • Potentially worse as traffic increases • Goal: Improve throughput by • Differentiating between various types of loss events • Estimating their probability of occurrence • Appropriately adapting

  4. Types of Loss 802.11 Network DCF – contention window Direct Collision (DC): nodes start transmitting in same slot Hidden Terminal Staggered: one node starts transmitting in the middle of another node’s packet SC1: node in question is first SC2: node in question is second Fading - Channel Errors Link adaptation, e.g. ARF increase rate after N consecutive successful packets decrease after M consecutive unsuccessful packets B A AP 4

  5. Components of Loss Probability • PSC2 = Probability of SC2 • PDC = Probability of DC given not SC2 • PSC1 = Probability of SC1 given not SC2 or DC • PC = Total Probability of collision • Pe = Probability of channel error • Component probabilities directly useful for link adaptation: • PSC2 most affected by sensing • PDC most affected by backoff • PSC1 most affected by packet length • Pe most affected by modulation rate

  6. Estimating Loss Probabilities  Last Review • Krishnan, Pollin, and Zakhor, “Local Estimation of Probabilities of Direct and Staggered Collisions in 802.11 WLANs”, IEEE Globecom 2009. • Basic idea: • Each nodes creates a local “busy-idle” signal for the channel • AP compresses and broadcasts its “busy-idle” signal periodically • Each node compares its local and AP “busy-idle” signal to estimate PSC2, PDC and PSC1. • Modified ns-2 • 7 APs, 50 randomly placed nodes • Poisson traffic with fixed rate, vary over simulations

  7. Overview • Background • Type of loss in wireless networks • Estimating collision probabilities • Using estimates to improve throughput • Modulation rate adaptation • Packet length adaptation • Future Work

  8. What to do with these estimates? Link adaptation: Current techniques assume all losses are due to channel error lower rate unnecessarily Make staggered collision problem worse  longer packets Adaptive packetization: if most collisions are staggered due to hidden nodes, need shorter packets Joint throughput optimization of: Modulation rate Packet length FEC Contention window Retransmit limit Transmit power Carrier sensing threshold Use of RTS/CTS Optimization might be different for delay Fairness issues 8

  9. Overview • Background • Type of loss in wireless networks • Estimating collision probabilities • Using estimates to improve throughput • Modulation rate adaptation • Packet length adaptation • Future Work

  10. Adapting Modulation Rate Using PC Estimate - COLA • Modified version of COLA1: State: For each rate, keep a pair (M,N) • Transmit at current rate for 5 seconds • Based on this data, estimate PC • Adjust (M,N) for this rate based on PC • Continue to transmit until M failed packets or N successes • Change rate and adjust (M,N) for previous rate • Go to 1. 1. Hyogon Kim, Sangki Yun, Heejo Lee, Inhye Kang, and Kyu-Young Choi, “A simple congestion-resilient link adaptation algorithm for IEEE 802.11 WLANs”, inProc. of IEEE GLOBECOM 2006, SanFrancisco, California, November 2006.

  11. Adapting Modulation Rate Using PC Estimate - SNRg • Algorithm • Transmit at current rate for 5 seconds • Based on this data estimate PC • Based on this PC and loss statistics, estimate Pe • Based on Pe and current rate, estimate average SNR • Change rate to theoretical best rate for current SNR • Go to 1.

  12. Simulation Setup Modified ns-2 802.11b infrastructure mode 7 AP’s with hexagonal cells 50 nodes placed by spatial Poisson process All nodes send saturated traffic to closest AP Run each algorithm using Pc estimates based on: Our estimation technique Empirical counting 12

  13. Throughput Improvement vs ARF(1,10) • Up to 5x throughput improvement when collisions are the only source of packet loss • Improvement decreases as channel error probability increases 32% improvement no improvement

  14. Per-node improvement  COLA y y • Greatest improvement close to AP • Distant nodes may have decreased throughput in high-noise environments APs nodes with increased throughput nodes with decreased throughput (circle size proportional to throughput change) x x -125dBm: 4.18x improvement -105dBm: 1.27x improvement

  15. Per-node improvement  COLA vs SNRg y y • High noise: -95dBm • Few nodes with significant change • SNRg outperforms COLA APs nodes with increased throughput nodes with decreased throughput (circle size proportional to throughput change) x x COLA: no improvement SNRg: 1.32x improvement

  16. Overview • Background • Type of loss in wireless networks • Estimating collision probabilities • Using estimates to improve throughput • Modulation rate adaptation • Packet length adaptation • Future Work

  17. How about packet length adaptation at the MAC-Layer? • Impact of packet size on effective throughput • Protocol header overhead • Larger packet size is preferable • Channel fading • Smaller packets are less vulnerable to fading errors • Direct collisions • Direct collision probability is independent of packet size • Staggered collisions in presence of hidden terminals • Smaller packets are less susceptible to collide with transmission from hidden terminals

  18. Packet Loss Model • Pure BER-based • Used in length adaptation literature • Assume constant BER over all packets over all time • Simple analysis • Does not account for packet-to-packet channel variation • Studied in: Song, Krishnan & Zakhor, “Adaptive Packetization for Error-Prone Transmission over 802.11 WLANs with Hidden Terminals”, IEEE MMSP 2009. • Mixed BER-SNR • Assume distribution on SNR: Rayleigh, Log-Normal, Rice • BER known function of SNR • Accounts for channel variation • BER is special case

  19. Analysis of Throughput vs Length for Mixed BER-SNR Model • Throughput ~ Data Rate x P(success) = Data Rate x (1-Pe) x (1-Psc1) Lp = payload length, Lh = header length, R = modulation rate, Tov = overhead, BER() functions are known • For single node, Psc1=0

  20. Single-Node Mixed BER-SNR Throughput vs Length Analysis – Varying Mean SNR Optimal packet length increases with SNR

  21. Single-Node Mixed BER-SNR Throughput vs Length Analysis – Varying SNR Variance Optimal packet length increases with SNR variance

  22. Single-Node Mixed BER-SNR Throughput vs Length Analysis – Rician and Rayleigh Fading Rician Rayleigh Similar effects with Rician/Rayleigh distributions

  23. Conclusions on Mixed BER-SNR Packet Loss Model • High SNR event more important than average SNR event for determining optimal packet length • Not sufficient to only consider average SNR or fixed BER • Ongoing work: • Optimal length as a function of SNR distribution • Analyze and characterize what scenarios can benefit from packet length adaptation • Extend to multiple nodes: • Increasing Tov to account for the increased average access time increases optimal length • Increase in SC1s decreases optimal length • Psc1 is a monotonic function of length  throughput vs length unimodal  search for optimum packet size

  24. Search for Optima Packet Length for Mixed BER-SNR Model • Random search: try different lengths and observe throughput • [Song et. al. MMSP’09] • May take long time to get accurate throughput estimates • Gradient search (Ongoing work): estimate gradient of throughput with respect to length to choose direction to move • May converge faster because of ability to move in more accurate direction with better step size • Requires estimation of gradient

  25. Computing Gradient of Throughput vs Length for Mixed BER-SNR Model (Ongoing Work) • Throughput ~ Data Rate x (1-Pe) x (1-Psc1) • Computing gradient requires estimation of each factor & its derivative: • First factor estimated by counting; • Second factor estimated from counting total losses and estimating Pc from [Krishnan et. al. Globecom’09]; • Third factor and its derivative estimated in [Krishnan et. al. Globecom’09]

  26. Joint Length and FEC Adaptation using Mixed BER-SNR Model (Future Work) • Decreasing length combats channel errors and SC1s. • If main problem is channel errors, i.e. few SC1s, adapt by adding FEC instead • New expression for (1-Pe): k<Lp number of FEC bits Ix(a,b) regularized incomplete beta function. • Assuming Lp is large, derivative w.r.t k is approximated

  27. Packet Loss Model • Pure BER-based • Commonly used in length adaptation literature • Assume constant BER over all packets over all time • Simple analysis • Does not account for packet-to-packet channel variation • Studied in: Song, Krishnan & Zakhor, “Adaptive Packetization for Error-Prone Transmission over 802.11 WLANs with Hidden Terminals”, IEEE MMSP 2009 • Mixed BER-SNR (Ongoing work) • Assume distribution on SNR (Rayleigh, Log-Normal, Rice) • BER is a known function of SNR • Accounts for channel variation • More general/realistic than BER model, which is a special case

  28. Packet Length Adaptation for Pure BER-Based Loss Model Simplified hidden node model: hidden nodes act independently of station in question

  29. Search Algorithm for Packet Size Initialize Lmin, Lmax, and L1 with Lmin < L1 < Lmax Apply L1 for packetization Measure throughput after Mt = 400 packet transmissions, recorded as Sn(1) Using golden section rule, choose L2 for packetization, L2 = L1 + C (Lmax - L1) Measure throughput after Mt = 400 packet transmissions, recorded as Sn(2) Compare Sn(1) and Sn(2) and use L1 or L2 to update Lmin or Lmax according to golden section rule Apply the steps recursively until Lmin and Lmax converge

  30. Network Simulations A1 B1 AP1 B2 A2 • Simulation topology • 20 middle nodes can sense all traffic • K hidden nodes at left side can sense transmissions from all nodes except the other K nodes at right side and vice versa • K = 2, 4, 6 • Saturated total traffic load • Memoryless packet erasure channel model • Consider packet loss due to direct collision, staggered collision and channel error • K sensing-limited nodes adapt packet length • Middle nodes send fixed-length background traffic

  31. Simulation Results Smaller packet size is selected for higher channel BER to reduce packet loss due to channel error Smaller packet size is selected in presence of more hidden nodes to reduce packet loss due to staggered collision

  32. Simulation Results: Effect on Collision Probabilities • Performance gain is due to trade-off among reduction of header overhead and packet loss • Primary Effect: staggered collision probability reduced significantly

  33. Simulation Results: Video Frame Delay 4 hidden nodes transmit an H.264-coded video sequence NBC 12 News at a mean coding rate of 497 kbit/s Average video frame transfer delay reduced from 85 ms to 24 ms

  34. Overview • Background • Type of loss in wireless networks • Estimating collision probabilities • Using estimates to improve throughput • Modulation rate adaptation • Packet length adaptation • Summary and future work

  35. Summary and Conclusions • Modulation Rate adaptation: • Using collision probability estimation  up to 5x throughput improvement in collision-limited scenarios • Packet length adaptation: • Pure BER-based model: staggered collisions have major effect • Up to 3x throughput improvement for SC-limited nodes • Mixed BER-SNR • Average SNR not sufficient statistic for selection of optimal packet length • Gradient of throughput with respect to packet length can be computed using collision probability estimation

  36. Future Work: Joint Adaptation of Additional Parameters • Modulation rate with Length/FEC • Appropriate length/FEC depends on rate since BER is function of SNR & modulation rate • Modulation rate highly discretized  can’t use gradient • Adapt modulation rate periodically, • Adapt length/FEC in-between adapting rate • Transmit power, carrier sense threshold, contention window • Optimize globally due to fairness issues • Can optimization be effectively distributed? • Can cheating be discouraged?

  37. Future Work: Other Uses of Collision Probability Estimates • Coping with collisions rather than avoiding them • Zig-Zag decoding [Katabi & Gollakota ’08] • Partial-packet recovery • Use of multiple paths in ad-hoc/mesh network • More paths  more resilient to channel errors, but increased traffic  more collisions • Effect on higher layers • TCP – collisions closer to congestion loss than fading loss

  38. Future Work: Experimental Verification • Universal Software Radio Peripheral (USRP2) + GNU Radio • Ported BBN 802.11 code for USRP to work for USRP2 • MadWifi • Accessed hardware registers to get “busy-idle” signal • Verifying consistency with packet pattern observed by sniffer, Kismet, in controlled environment

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