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A General Model of Wireless Interference

A General Model of Wireless Interference. L. Qiu, Y. Zhang, F. Wang, M. Han, R. Mahajan Mobicom 2007. A Model for ?. Misleading title Nothing new about wireless interference Indeed, a model for predicting the throughput/goodput of wireless networks Motivation

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A General Model of Wireless Interference

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  1. A General Model of Wireless Interference L. Qiu, Y. Zhang, F. Wang, M. Han, R. Mahajan Mobicom 2007

  2. A Model for ? • Misleading title • Nothing new about wireless interference • Indeed, a model for predicting the throughput/goodput of wireless networks • Motivation • Helpful in evaluating design/protocols (e.g. channel assignment) • Direct measurements alone is insufficient • Lacks predictive power and scalability

  3. Problem Statement Bn • Given characteristics of • RSS between each pair (RSSm,n) • Background noise (Bn) • Traffic demand between pairs (dm,n) • What is the pairwise throughput/goodput? • My dumb solution • Calculate SINR at every node • Throughput = B*log2(1+SINR) RSSm,n • Problems • Non-constant RSS • Ignoring Underlying MAC

  4. Contributions • State of the Art: only handle restricted traffic • Only two senders or two flows • Only broadcast traffic • Only saturated demands • Contributions • Interference among an arbitrary number of senders • Both broadcast and unicast traffic • Both saturated and unsaturated demand Its limit State of the Art This paper Reality Dumb solution Limit of analytical methods

  5. Overview of the Model sender model given network RF profile measurement pairwise RSS throughput • How it works • Measure pairwise RSS via broadcast probes • One node broadcast at a time, others measure RSS  O(n) probes • Saturated broadcast sender/receiver models • Markov-chain model • Extend to unsaturated broadcast • Extend to saturated/unsaturated unicast traffic demand receiver model goodput

  6. Broadcast Sender: Overview • Estimate how much a sender can send • MAC: 802.11 DCF • Markov chain (simplification #1) • State i: a set of active nodes Si • Stationary probabilities: i(fraction of time that the system is in state i) • Throughput of node m: tm = ∑i|mSi i 00 01 0…1 0…0 0..10 . . 10 11 . 1…1 .

  7. Broadcast Sender: Overview (Cont.) • State transition probability • Staying idle:P00(n|Si) • Idle to active:P01(n|Si) • Active to idle:P10(n|Si) • Staying active:P11(n|Si) • Assume node independence (simplification #2) • Compute stationary probabilities i by solving LP • Highly efficient for sparse M

  8. Broadcast Sender: Transition Probabilities Simplification #3 Under the assumption that both transmission and idle times are exponential (simplification #4)

  9. Broadcast Sender: Clear Probability • How to estimate Im|Si? • Im|Si=Wm+Bm+∑sSi\{m}Rsm • Assume each term is lognormal variable (simplification #5) • Approximate the sum using a lognormal variable by matching mean and variance

  10. Broadcast Sender: Handle Similar Packet Sizes • Synchronization occurs when packet sizes used by different nodes are similar • When several nearby nodes transmit together, they will end transmission together • Independence assumption fails • Handle synchronization • Construct synchronization graph Gsyn • Two nodes are connected iff C(m|{n})  0.1 and C(n|{m})  0.1 • Find all synchronization groups • Each connected component in Gsyn is a synchronization group (simplification #6) • If m and n in the same synchronization group • mSj and n Sj’ M(i,j) = 0 • P10(mn|Si) = Tslot/T(m) instead of (Tslot/T(m))|G|

  11. Broadcast Sender: Handle Unsaturated Demands • Estimate Q(m): probability m has data to send when its backoff counter is 0 and channel is clear at m • Under saturated demands, Q(m) = 1 • Under unsaturated demands, compute Q(m) iteratively to ensure that demands are not exceeded Initialize Q(m) = 1 Solve the Markov chain Update Q(m)

  12. Brief overview of other parts • Broadcast Receiver • Estimate packet loss rate • Extending to unicast: challenges • Binary backoff • Sending rate depends on loss rates • DATA losses due to collisions with ACKs • Model ACK sending rate, which in turn depends on DATA sending rate and loss rates • Traffic demand • Account for retransmissions

  13. Simulation Evaluation: Saturated Broadcast 2 saturated broadcast (a) throughput (b) goodput More accurate than UW 2-node model

  14. Simulation Evaluation: Saturated Broadcast 10 saturated broadcast (a) throughput (b) goodput Accurate for 10 saturated broadcast

  15. Testbed Evaluation UW traces: 2 senders, 30 mW, broadcast, saturated (b) goodput (a) throughput More accurate than UW-model for 2-sender

  16. Summary • A model for predicting the throughput of wireless networks • Validated by simulation and testbed evaluation Its limit State of the Art This paper Less simplifications RTS/CTS, different MAC Traffic modelling, human behavior Reality Dumb solution Limit of analytical methods

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