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 Using estimates to improve throughput Modulation rate adaptation Packet length adaptation Future Work Participants Dr. Wei Song Colby Boyer Miklos Christine Sherman Ng 2

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: Mbps −Potentially worse as traffic increases Goal: Improve throughput by Differentiating between various types of loss events Estimating their probability of occurrence Appropriately adapting 3

4 Types of Loss 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 4 A B AP

Components of Loss Probability P SC2 = Probability of SC2 P DC = Probability of DC given not SC2 P SC1 = Probability of SC1 given not SC2 or DC P C = Total Probability of collision P e = Probability of channel error Component probabilities directly useful for link adaptation: P SC2 most affected by sensing P DC most affected by backoff P SC1 most affected by packet length P e most affected by modulation rate 5

Estimating Loss Probabilities  Last Review Krishnan, Pollin, and Zakhor, “Local Estimation of Probabilities of Direct and Staggered Collisions in WLANs”, IEEE Globecom 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 P SC2, P DC and P SC1. 6 Modified ns-2 7 APs, 50 randomly placed nodes Poisson traffic with fixed rate, vary over simulations

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

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 8 Fairness issues Data Rate 1-P e 1-P SC2 1-P DC 1-P SC1 Tx Power ++ CS Thresh -+ Contention Window -+ Modulation Rate +-+/- Length +-- FEC -+ RTS/CTS -++

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

Adapting Modulation Rate Using P C Estimate - COLA Modified version of COLA 1 : State: For each rate, keep a pair (M,N) 1.Transmit at current rate for 5 seconds 2.Based on this data, estimate P C 3.Adjust (M,N) for this rate based on P C 4.Continue to transmit until M failed packets or N successes 5.Change rate and adjust (M,N) for previous rate 6.Go to Hyogon Kim, Sangki Yun, Heejo Lee, Inhye Kang, and Kyu-Young Choi, “A simple congestion-resilient link adaptation algorithm for IEEE WLANs”, inProc. of IEEE GLOBECOM 2006, SanFrancisco, California, November 2006.

Adapting Modulation Rate Using P C Estimate - SNRg Algorithm 1.Transmit at current rate for 5 seconds 2.Based on this data estimate P C 3.Based on this P C and loss statistics, estimate P e 4.Based on P e and current rate, estimate average SNR 5.Change rate to theoretical best rate for current SNR 6.Go to 1. 11

12 Simulation Setup Modified ns b 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

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 13 32% improvement no improvement

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

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

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

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

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 WLANs with Hidden Terminals”, IEEE MMSP Mixed BER-SNR Assume distribution on SNR: Rayleigh, Log-Normal, Rice BER known function of SNR Accounts for channel variation BER is special case 18

Analysis of Throughput vs Length for Mixed BER-SNR Model Throughput ~ Data Rate x P(success) = Data Rate x (1-Pe) x (1-Psc1) L p = payload length, L h = header length, R = modulation rate, T ov = overhead, BER() functions are known For single node, Psc1=0 19

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

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

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

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 T ov 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 23

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 24

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] 25

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 I x (a,b) regularized incomplete beta function. Assuming Lp is large, derivative w.r.t k is approximated 26

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 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 27

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

Search Algorithm for Packet Size Initialize L min, L max, and L 1 with L min < L 1 < L max Apply L 1 for packetization Measure throughput after M t = 400 packet transmissions, recorded as S n (1) Using golden section rule, choose L 2 for packetization, L 2 = L 1 + C (L max - L 1 ) Measure throughput after M t = 400 packet transmissions, recorded as S n (2) Compare S n (1) and S n (2) and use L1 or L2 to update L min or L max according to golden section rule Apply the steps recursively until L min and L max converge 29

30 Network Simulations 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 B2 AP1 A1 A2 B1

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

33 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 Simulation Results: Video Frame Delay

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 34

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 35

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? 36

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 37

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