Throughput Improvement in 802.11 WLANs using Collision Probability Estimates

1. Throughput Improvement in 802.11 WLANs using Collision Probability Estimates AvidehZakhor E. Haghani, M. Krishnan, M. Christine, S. Ng Department of Electrical Engineering and Computer Sciences U.C. Berkeley October 2010

2. 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

3. 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

4. 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) B A AP 4

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 PC C A AP1 AP2 B Node A: AP1: Node B: Krishnan, Pollin, and Zakhor, “Local Estimation of Probabilities of Direct and Staggered Collisions in 802.11 WLANs”, IEEE Globecom 2009.

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 Fairness issues 6

7. 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

8. 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 • 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.

9. 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 seconds • Station generates its own BI signal • Depends on CS threshold ϒ. • For p, q ∈ {0, 1},

10. 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 Exposed node transmission Hidden node transmission

11. 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: • P10 decreases – fewer exposed nodes • P01 increases – more hidden nodes

12. 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.

13. 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

14. 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.

15. 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

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

17. 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

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

19. 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 L = payload length Lh = header length Rp = payload modulation rate Rh = header modulation rate f () = distribution of SNR BER() functions are known

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

21. 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

22. 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

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

24. 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 Node 1 Node 2

25. Example of Adapted Length and Throughput Change • 7 APs, 50 nodes, -89 dBm noise • Periphery nodes choose shorter lengths • Spatial correlation between gain/loss • Highest % gain in T.P lowest absolute T.P. nodes Length % throughput change Total throughput =gain =loss =standard =adaptive

26. Throughput Improvement vs Noise Power • High noise power  High Pe  more nodes choose smaller L -89 dBm -95 dBm

27. 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

28. 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

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 C A AP1 AP2 B Node A: AP1: Node B:

30. 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

31. 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

32. Generating BI/TX signals Candidate busy section is set of consecutive y-values (RXCLR or TX) which are strictly increasing: Equation 1: +lower bound ×upper bound b

33. Aligning Station and AP signals • 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

34. 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

35. Experimental Results • 75 total estimates: • 5 levels of Pc with 15 estimates each: • 6 estimates with Pe~0 • 9 estimates with 20<Pe<40 =low Pe X =high Pe Pc estimates are within 5% accuracy

36. 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?

37. 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

38. 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? Internet 1 2 4 3 AP client server asymmetry