Throughput Improvement in 802.11 WLANs using Collision Probability

Estimates Avideh Zakhor

E. Haghani, M. Krishnan, M. Christine, S. Ng

Department of Electrical Engineering and Computer Sciences

U.C. BerkeleyOctober 2010

OutlineBackground

• Type of loss in wireless networks• Estimating collision probabilities two years ago

Using estimates to improve throughput• Modulation rate adaptation last year• This year:

−Carrier sense threshold−Packet length adaptation−Experimental verification

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Motivation & Goal

Improve throughput:• Differentiate between various loss events• Estimate probability of occurrence of each type• Adapt:

−Link adaptation algorithm−Packet length−Carrier sense threshold−Contention window − Transmit power −FEC

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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 Collision: one node starts transmitting in the

middle of another node’s packet− SC1: node in question is first− SC2: node in question is second

Channel Errors• Large pathloss due to distance/obstacles (large timescale)• Random multipath fading (small timescale)

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Node A packet

Node B packet

A BAP

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Estimating Collision Probability Each node/AP collects binary-valued ‘busy-idle’ (BI) signal

• 1 when local channel is occupied, 0 otherwise AP broadcasts its BI signal periodically ~14kb/s, 3% overhead Nodes use their BI signal along with AP’s to estimate PC

Node A:

Node B:

AP1:

AB

AP1 CAP2

Krishnan, Pollin, and Zakhor, “Local Estimation of Probabilities of Direct and Staggered Collisions in 802.11 WLANs”, IEEE Globecom 2009.

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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 - + +

OutlineBackground

• Type of loss in wireless networks• Estimating collision probabilities two years ago

Using estimates to improve throughput• Modulation rate adaptation last year• This year:

−Carrier sense threshold−Packet length adaptation−Experimental verification

7

Carrier Sense Optimization in 802.11 CSMA network - nodes

transmit only if sensed power < CS threshold

Trade-off between hidden node problem and exposed node problem

CS threshold => # of hidden nodes , # of exposed nodes

Tune CS threshold to:• minimize # of hidden

nodes + # of exposed nodes for the transmitter

• Increase throughput8

• A (the Station) is transmitting to B (the AP).

• : transmission range -- Signal can be decoded

• : CS range -- Received power > CS threshold)

• : interference range -- Any transmission in this range collides with A’s signal at B

• E is an exposed node and F is a hidden node to A.

tr

cr

ir

Busy/Idle Signal AP broadcasts its BI signal, BIAP, every Δ seconds Each station records multi-leveled sensed energy

level for the same period of Δ secondsStation generates its own BI signal

• Depends on CS threshold ϒ.For p, q ∈ {0, 1},

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Ppq (t) :Pr{BISTA (t) p,BIAP (t) q}

BISTA

Hidden and Exposed nodes in BI signal Hidden node problem: BISTA = 0 and BIAP = 1 => collisions Exposed node problem: BISTA = 1 and BIAP = 0 => excess backoff Continuous-valued sensed power depends on other nodes

sending, but node can affect binary-valued BISTA by adapting CST• BISTA = 1{power > CST}

Adapt to minimize + , or

10Hidden node transmission

Exposed node transmission

P01i P10

i

Optimization FunctionHidden and exposed nodes reduce the throughputCan affect number of hidden and exposed nodes by

tuning ϒ |Transmissions of Hidden Nodes| ∝

|Transmissions of Exposed Nodes| ∝ Optimization:

whereAs increases:

• P10 decreases – fewer exposednodes

• P01 increases – more hiddennodes

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iP01

iP10

)(minarg iiopt Fi

ii PPF ii 1001)(

Algorithm Record energy level of the

channel for Δ=3 seconds. Receive BI signal from AP. Calculate the value of function

F for all possible values of carrier sense threshold.

Find the value of the carrier sense threshold that minimizes F.

Find the value of F for the previous value of carrier sense threshold.

If the difference is more than 5% of previous value change the carrier sense threshold.

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Simulation Setup7 APs, 50 nodesAPs have fixed CST for each simulation

• Different over various simulations2 methods for comparison:

• Nodes have same fixed CST as APs• Nodes asynchronously adapt using our algorithm:

−Use current CST for 3+ seconds, where is random−Solve optimization for data from most recent 3 seconds

Consider all nodes in 10 different 60-second simulations with different topologies 500 total nodes• Repeat this for each value of AP CST

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Simulations: Aggregate Throughput vs AP CST

Up to 50% total throughput improvement• Moderate decrease when AP CST is very low – single

collision domain The average of log-throughput is increased in all scenarios

=> adaptive CST algorithm behaves fairly.

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Simulation Results: Node Throughput

80% of nodes gain throughput, only 10% loseMedian: 81%, Mean: 131% Improvement depends on locations of hidden and

exposed nodes15

Simulation Result: Attempts and Losses Adaptive algorithm results in:

• Lower loss probability• Fewer transmission attempts

More efficient channel use

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OutlineBackground

• Type of loss in wireless networks• Estimating collision probabilities two years ago

Using estimates to improve throughput• Modulation rate adaptation last year• This year:

−Carrier sense threshold−Packet length adaptation−Experimental verification

17

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Effects of MAC Layer Packet Length 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

BER-SNR• Assume constant BER over each packet• Assume distribution on SNR: Rayleigh, Log-Normal, Rice• BER known function of SNR and modulation rate• Accounts for channel variation• Pure BER is special case where SNR distribution is delta

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L = payload length Lh = header lengthRp = payload modulation rate Rh = header modulation ratef () = distribution of SNR BER() functions are known

Single-Node Throughput vs Length as a function of BER-SNR Variance

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Optimal packet length increases with SNR variance

Approach: Gradient Search

TP = throughput L = packet length sendFreq =# packets/sec

PSC1 = P(SC1) Pe = P(channel error) C’ constantGradient of TP w.r.t. packet length:

Pe estimated as:

L known; sendFreq and PL empirical counting, m2 and Pc [1] next page 21

Estimating

where = P(error for packet with SNR )

= P(header error for packet with SNR ) Estimate Pe from [1] look up Assume single parameter or two parameter

distribution22

AlgorithmObserve for N seconds without adaptation, EstimateAdjust L by where is adjusted as

follows:

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Verification of via NS SimulationsScenario: 7 Aps, 50 nodes, all using constant packet

length• Vary L for a single node to examine TP vs L• Locally compute and compare to slope of empirical

TP vs L curve

24Node 1 Node 2

Example of Adapted Length and Throughput Change

• Periphery nodes choose shorter lengths

• Spatial correlation between gain/loss

• Highest % gain in T.P lowest absolute T.P. nodes

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Length

% throughput change Total throughput

=gain =loss =standard =adaptive

7 APs, 50 nodes, -89 dBm noise

Throughput Improvement vs Noise PowerHigh noise power High Pe more nodes choose

smaller L

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-89 dBm -95 dBm

OutlineBackground

• Type of loss in wireless networks• Estimating collision probabilities two years ago

Using estimates to improve throughput• Modulation rate adaptation last year• This year:

−Packet length adaptation−Carrier sense threshold−Experimental verification

27

Experimental Verification of Pc Estimation Implemented mechanism behind collision probability

estimation technique using Ath5k open source wireless card driver

Topology:

• Node 1 sends to AP 1, and computes estimates• Node 2 sends to AP 2 to cause hidden node collisions• Sniffers observe ground truth

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Estimation Approach – ‘Busy-Idle’ Signal Each node/AP collects binary-valued ‘busy-idle’ (BI) signal

• 1 when local channel is occupied, 0 otherwise Also collect TX signal - 1 when transmitting, 0 otherwise

Node A:

Node B:

AP1:

AB

AP1 CAP2

System Design4 steps:

• Collect available carrier sense data from wireless card• Process this data to generate BI and TX signals• Align BI and TX signals of station and AP• Compute estimates

Ideally completely implemented at driver levelCurrent implementation only collects data in real time

• Data is processed offline in MATLAB

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Collecting Carrier Sense DataAccess “profile count” registers; observe this

behavior:• AR5K_POFCNT_CYCLE: constantly incrementing like

clock• AR5K_PROFCNT_TX: increasing at same rate at CYCLE

when transmitting, constant otherwise• AR5K_PROFCNT_RXCLR: increasing at same rate at

CYCLE when channel is occupied, constant otherwise In theory:

• BI signal is slope of RXCLR vs CYCLE• TX signal is slope of TX vs CYCLE

Practically: can capture• sequentially – not simultaneously • not necessarily regularly• “time” of TX or RXCLR sample is bounded by value of

previous and subsequent sampled value of CYCLE31

Generating BI/TX signals

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Candidate busy section is set of consecutive y-values (RXCLR or TX) which are strictly increasing:

Equation 1:

b

+ lower bound× upper bound

Aligning Station and AP signals

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To estimate collision probability, need to line up TX and BI signals between station and AP

Scale to adjust for different clock speeds Use large scale view of packet start times

Align TX signals more sparse than BI signals; easier• AP TX consists of ACKS, some of them to station• Line up inter-packet times

BI signals follow since they are collected on same clock as TX

Most packets aligned within 40s of each other

Experimental SetupTopology:

• Node 1 sends to AP 1, and computes estimates• Node 2 sends to AP 2 to cause hidden node collisions• Sniffers observe ground truth

Variables:• Transmit power of node 1

−to affect Pe• Sending rate of node 2

−to affect Pc

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Experimental Results75 total estimates:

• 5 levels of Pc with 15 estimates each:−6 estimates with Pe~0−9 estimates with 20<Pe<40

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=low Pe X =high Pe

Pc estimates are within 5% accuracy

Future Work: Contention Window AdaptationContention Window Adaptation strategy:

• Nodes wait for random number, drawn uniformly from {1,2,…,W} of idle time slots before transmitting

• If packet fails, WW• By default =2

Can show this is asymptotically optimal as n for single collision domain with no fading/noise, i.e. all losses are DCs

What happens when we include other types of losses in the model?• E.g. if all losses are due to channel, want =0

What about more general schemes where we can choose arbitrary distributions for backoff time?

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Future Work: Delay-sensitive traffic Effective throughput – throughput received within delay bound What bit rate & retransmit limit (γ) & delay limit (τ) maximize

the effective throughput (η)?

Derive an analytical expression/model for effective throughput• Use the BI signal information• Nodes make observations to estimate parameters of model

Advantages:• Can adapt fast in multi-dimensional parameter space• Preferable to making one parameter at a time observations of

throughput

pktstxRcvd

#limitdelay within pkts #

Future Work: Application to asymmetric TCP

Links 1,4 subject to network congestionLink 2 subject to channel errorsLink 3 subject to channel errors AND collisionsTCP assumes symmetric channel only limited by

congestion Question: Can we take advantage of knowing collision

probability to adjust parameters of asymmetric TCP algorithms?• low Pc => channel is roughly symmetric• higher Pc => increased asymmetry? 38

Internet

client AP server

1

4

2

3

asymmetry