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![Page 1: Throughput Improvement in 802.11 WLANs using Collision Probability Estimates Avideh Zakhor E. Haghani, M. Krishnan, M. Christine, S. Ng Department of Electrical.](https://reader035.fdocuments.us/reader035/viewer/2022062309/56649cd95503460f949a293d/html5/thumbnails/1.jpg)
Throughput Improvement in 802.11 WLANs using Collision Probability
Estimates
Avideh ZakhorE. Haghani, M. Krishnan, M. Christine, S. Ng
Department of Electrical Engineering and Computer Sciences
U.C. BerkeleyOctober 2010
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Outline
Background• 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:
A
B
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 - + +
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Outline
Background• 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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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 throughput
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• 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
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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
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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
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Optimization Function
Hidden 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 exposed
nodes
• P01 increases – more hidden
nodes
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iP01
iP10
)(minarg iiopt Fi
ii PPF ii 1001)(
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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 Setup
7 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
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Simulation Result: Attempts and Losses
Adaptive algorithm results in:• Lower loss probability• Fewer transmission attempts
More efficient channel use
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Outline
Background• 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
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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
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Single-Node Throughput vs Length as a function of BER-SNR Variance
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Optimal packet length increases with SNR variance
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Approach: Gradient Search
TP = throughput L = packet length sendFreq =# packets/sec
PSC1 = P(SC1) Pe = P(channel error) C’ constant
Gradient of TP w.r.t. packet length:
Pe estimated as:
L known; sendFreq and PL empirical counting,
m2 and Pc [1] next page 21
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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
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Algorithm
Observe for N seconds without adaptation, EstimateAdjust L by where is adjusted as
follows:
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Verification of via NS Simulations
Scenario: 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
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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
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Throughput Improvement vs Noise Power
High noise power High Pe more nodes choose smaller L
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-89 dBm -95 dBm
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Outline
Background• 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
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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:
A
B
AP1 C
AP2
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System Design
4 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 Data
Access “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
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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
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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
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Experimental Setup
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
Variables:• Transmit power of node 1
−to affect Pe
• Sending rate of node 2−to affect Pc
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Experimental Results
75 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
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Future Work: Contention Window Adaptation
Contention 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
pktstx
Rcvd
#
limitdelay within pkts #
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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?
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Internet
client APserver
1
4
2
3
asymmetry