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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240
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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140
160
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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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200
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600
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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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100
200
300
400
500
600
700
800
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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80
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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