Throughput Enhancement in Wireless LANs via Loss Differentiation

Michael Krishnan, Avideh ZakhorDepartment of Electrical Engineering and Computer

SciencesU.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

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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

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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

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Node A packet

Node B packet

A BAP

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 errorComponent 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

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)1()1()1( eCL PPP )1()1()1()1( 12 eSCDCSC PPPP

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.

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•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

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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

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Fairness issues

Data Rate

1-Pe 1-PSC2 1-PDC 1-PSC1

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

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Adapting Modulation Rate Using PC Estimate - COLA

Modified version of COLA1:State: For each rate, keep a pair (M,N)1.Transmit at current rate for 5 seconds2.Based on this data, estimate PC

3.Adjust (M,N) for this rate based on PC

4.Continue to transmit until M failed packets or N successes

5.Change rate and adjust (M,N) for previous rate6.Go to 1.

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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.

Adapting Modulation Rate Using PC Estimate - SNRg

Algorithm1. Transmit at current rate for 5 seconds2. Based on this data estimate PC

3. Based on this PC and loss statistics, estimate Pe

4. Based on Pe and current rate, estimate average SNR

5. Change rate to theoretical best rate for current SNR6. Go to 1.

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Simulation Setup

Modified ns-2802.11b infrastructure

mode7 AP’s with hexagonal

cells50 nodes placed by

spatial Poisson processAll nodes send saturated

traffic to closest APRun each algorithm using

Pc estimates based on:• Our estimation technique• Empirical counting

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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

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32% improvement

no improvement

Per-node improvement COLA

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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

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APs nodes with increased throughput nodes with decreased throughput(circle size proportional to throughput change)

x

y

x

y

COLA: no improvement SNRg: 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

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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 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

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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 knownFor single node, Psc1=0

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s

Lp

Lhe dssBERsBERsSNRPP ph ))(1())(1)((1

ovp

p

TRL

LDataRate

/

Single-Node Mixed BER-SNR Throughput vs Length Analysis – Varying Mean SNR

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Optimal packet length increases with SNR

Single-Node Mixed BER-SNR Throughput vs Length Analysis – Varying SNR Variance

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Optimal packet length increases with SNR variance

Single-Node Mixed BER-SNR Throughput vs Length Analysis – Rician and Rayleigh Fading

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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 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

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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

estimatesGradient 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

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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]

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L(1 Pe ) P(SNRs)ln(1 BERp (s))(1 BERp (s))

L p (1 BERh (s))Lh ds

s

LDataRate

Tov(Lp /R Tov )

2

i i

ixx

ii xf

xfxfxfxfxf

)(

)()()()()(

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 insteadNew 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

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s

psBERL

h

s

k

i

iLp

ip

pLhe

dskkLIsBERsSNRP

dssBERsBERi

LsBERsSNRPP

p

h

ph

)1,())(1)((

))(1()())(1)((1

)(1

0

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

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Packet Length Adaptation for Pure BER-Based Loss Model

Simplified hidden node model: hidden nodes act independently of station in question

0 500 1000 1500 2000100

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MAC-layer transmission packet size (byte)

Effe

ctiv

e th

rou

gh

pu

t (kb

it/s)

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

0 500 1000 1500 2000100

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MAC-layer transmission packet size (byte)

Effe

ctiv

e th

rou

gh

pu

t (kb

it/s)

2 4 6 8 10 12 140

500

1000

1500

2000

Search step

Tra

nsm

issi

on

pa

cke

t siz

e (

byt

e)

Lmin

Lmax

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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

AP1AP1

A1

A2

B1

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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

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MAC-layer transmission packet size (byte)

Th

rou

gh

tpu

t of o

ne

hid

de

n n

od

e (

kbit/

s) With BER 2E-5

With BER 4E-5

With BER 1E-4

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MAC-layer transmission packet size (byte)

Th

rou

gh

tpu

t of o

ne

hid

de

n n

od

e (

kbit/

s) With 4 hidden nodes With 8 hidden nodes With 12 hidden nodes

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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

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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

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System time (s)

Vid

eo

fra

me

tra

nfe

r d

ela

y (m

s)

With searched packet sizeWith maximum packet size

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

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Summary and Conclusions

Modulation Rate adaptation:• Using collision probability estimation up to 5x

throughput improvement in collision-limited scenariosPacket 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

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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?

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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 collisionsEffect on higher layers

• TCP – collisions closer to congestion loss than fading loss

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Future Work: Experimental Verification

Universal Software Radio Peripheral (USRP2) + GNU Radio• Ported BBN 802.11 code for USRP to work for USRP2

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MadWifi• Accessed hardware

registers to get “busy-idle” signal

• Verifying consistency with packet pattern observed by sniffer, Kismet, in controlled environment