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Throughput Improvement in WLANs using Collision Probability Estimates Avideh Zakhor E. Haghani, M. Krishnan, M. Christine, S. Ng Department of Electrical Engineering and Computer Sciences U.C. Berkeley October 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 2

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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 3

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4 Types of Loss 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) 4 A B AP

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5 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 P C Node A: Node B: AP1: A B AP1 C AP2 Krishnan, Pollin, and Zakhor, “Local Estimation of Probabilities of Direct and Staggered Collisions in WLANs”, IEEE Globecom 2009.

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6 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 6 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 -++

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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 7

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Carrier Sense Optimization in 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 8 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.

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Busy/Idle Signal AP broadcasts its BI signal, BI AP, every Δ seconds Each station records multi-leveled sensed energy level for the same period of Δ seconds Station generates its own BI signal Depends on CS threshold ϒ. For p, q ∈ {0, 1}, 9

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Hidden and Exposed nodes in BI signal Hidden node problem: BI STA = 0 and BI AP = 1 => collisions Exposed node problem: BI STA = 1 and BI AP = 0 => excess backoff Continuous-valued sensed power depends on other nodes sending, but node can affect binary-valued BI STA by adapting CST BI STA = 1{power > CST} Adapt to minimize +, or 10 Hidden node transmission Exposed node transmission

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Optimization Function Hidden and exposed nodes reduce the throughput Can affect number of hidden and exposed nodes by tuning ϒ |Transmissions of Hidden Nodes| ∝ |Transmissions of Exposed Nodes| ∝ Optimization: where As increases: P 10 decreases – fewer exposed nodes P 01 increases – more hidden nodes 11

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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. 12

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Simulation Setup 7 APs, 50 nodes APs have fixed CST for each simulation Different over various simulations 2 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 13

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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. 14

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Simulation Results: Node Throughput 80% of nodes gain throughput, only 10% lose Median: 81%, Mean: 131% Improvement depends on locations of hidden and exposed nodes 15

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Simulation Result: Attempts and Losses Adaptive algorithm results in: Lower loss probability Fewer transmission attempts More efficient channel use 16

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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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18 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 19 L = payload lengthL h = header length R p = payload modulation rate R h = header modulation rate f () = distribution of SNRBER() functions are known

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Single-Node Throughput vs Length as a function of BER-SNR Variance 20 Optimal packet length increases with SNR variance

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Approach: Gradient Search TP = throughput L = packet length sendFreq =# packets/sec P SC1 = P(SC1) P e = P(channel error) C’ constant Gradient of TP w.r.t. packet length: P e estimated as: L known; sendFreq and P L empirical counting, m 2 and P c [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 distribution 22

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Algorithm Observe for N seconds without adaptation, Estimate Adjust L by where is adjusted as follows: 23

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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 24 Node 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 25 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 P e more nodes choose smaller L 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 27

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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 28

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29 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 level Current implementation only collects data in real time Data is processed offline in MATLAB 30

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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 CYCLE 31

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Generating BI/TX signals 32 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 33 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 40 s 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 34

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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

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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, W W 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? 36

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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

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Future Work: Application to asymmetric TCP Links 1,4 subject to network congestion Link 2 subject to channel errors Link 3 subject to channel errors AND collisions TCP 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 asymmetry

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