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THROUGHPUT ANALYSIS OF IEEE 802.11 DCF BASIC IN PRESENCE OF HIDDEN STATIONS

THROUGHPUT ANALYSIS OF IEEE 802.11 DCF BASIC IN PRESENCE OF HIDDEN STATIONS. Shahriar Rahman Stanford Electrical Engineering http://ee.stanford.edu. Outline of Talk. 802.11 DCF Protocol Overview Problem with DCF Basic Access Modeling Hidden Stations DCF Throughput Models

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THROUGHPUT ANALYSIS OF IEEE 802.11 DCF BASIC IN PRESENCE OF HIDDEN STATIONS

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  1. THROUGHPUT ANALYSIS OF IEEE 802.11 DCF BASIC IN PRESENCE OF HIDDEN STATIONS Shahriar Rahman Stanford Electrical Engineering http://ee.stanford.edu

  2. Outline of Talk • 802.11 DCF Protocol Overview • Problem with DCF Basic Access • Modeling Hidden Stations • DCF Throughput Models • Simulation Results • Discussions & Conclusion • Future Work • Q&A

  3. IEEE 802.11 DCF • 802.11 operates on DSSS, FHSS or IR PHY • MAC provides CSMA/CA through NAV (~’CS’) • Basic & RTS/CTS accesses • Congestion, timing and backoff mechanisms • On modeling DCF ->Bianchi; Wu, et. al.

  4. C D A B A Problem with DCF Basic • 2-way handshaking • Assumes that there is no other transmission during this slot!!! • What if there is a hidden station???

  5. Saturation Throughput Model • Bianchi provides a saturation throughput model based on a Markov model of backoff mechanism- Psuccess E[P] Pidles + Psuccess Ts + Pcollision Tc • Pidle = 1- PtrandPsuccess = Ptr Ps • Pcollision = Ptr (1 - Ps) • Ptr = 1 – (1 – t) nandPs = nt (1 – t) n-1 /Ptr • Tsand Tc measures time durations of a successful transmission and collided transmission S =

  6. Hidden Station Model - Static • Kleinrock and Tobagi’s hearing graph- 11 1 0 0 1 2 1 1 0 0 1 3 0 0 1 1 1 4 0 0 1 1 0 5 0 1 0 0 1 • Each station can hear some and not others => Pr(reachable) with assumption static => no transition • Generalize this to an n-station WLAN and decompose into a k-group reachability graph- Pr(n) = S(Nr(j) /Nt(j) ) / k • Take average stations per group => expected number of hidden stations in the network 1, 2 3, 4 5 (a) (b) (c)

  7. Hidden Station Model - Dynamic Adjacencygraph 2 1 2 k 4 1 3 k-state Markov chain • Extend static model and allow transitions between k states, over n stations? => adjacency graph • Pr(reachable->reachable)=> use control parameter, m • Pr(hidden->*) = 1/l, Pr(reachable->hidden) = (1-m)/(l-1) • Balance equations: Pr(j) + (1 – l) Ph(j) = 1 (1 - m)/(1 - l) Pr(j) = (1/l) Ph(j) • Solve to get: Pr(j) = 1 / (1 + l(1 – m))

  8. Our Throughput Model - Saturation • Worst case throughput loss => hidden stations always transmit • Ptr = 1;Ps = Nret (1 – t) Nre-1 • This changes throughput to- PsE[P]/(PsTs + PcollTc) • I also changed Tc to include ACK_Timeout- DIFS+E[P]+SIFS+ACK_.. • Huge degradation of throughput for either static or dynamic WLANs • Will see simulations agree

  9. Our Throughput Model – Finite Load(1) • Similar grouping into k groups, but now with identical loads, li individually and Sli = lper group • Packet from a group must be successful both from its group and all other groups- • Further, transmission probabilities from k contending groups consisting some stations each • Plug Psand Ptr into throughput equation • Can be used for both basic and RTS/CTS

  10. Our Throughput Model – Finite Load(2) • Now have hidden groups, but assume same rate per group persists (i.e. allow only same rate within group) • Extend the previous Psand Ptr to separate out reachable and hidden stations, in adjacency graph, i.e., • Assumption that reachable >= hidden. Is it valid? • It is not obvious how to calculate t. One idea may be from scheduler’s history at stations • Certainly justifies RTS/CTS, MACAW, DCF+, etc.

  11. Simulation Topology & Traffic • Simulations in ns-2 • 914MHz Lucent WaveLAN DSSS PHY • Omni-antenna with 250m range • Modified CMU scene generator to create hidden stations, static topology, random pause time • Modified CMU traffic generator for variable packet size, intervals 2 <=250m 4 1 3 5 >250m • RTS threshold => 3000 bytes • 1028 bytes (8224 bits) packets • Inter-packet gap = 0 (saturation) and 1/rate (finite load) • CBR traffic over UDP links • Script to calculate various throughputs from trace

  12. Saturation Simulation Results • Simulated with certain percentage hidden stations for 5, 10, 20, 50 stations • Results agree with model to some extent • Differences can be attributed to hidden stations may not always have packets (as assumed in the model) • Still need to experiment with m and simulate finite load throughput

  13. Discussions & Conclusion • Hidden station models are sophisticated and can be used in many applications involving “carrier sense” • Saturation throughput model is valid and should be considered as an extension to Bianchi’s DCF model • Proposed finite load model is computationally expensive and needs further simplification. Finite load throughput model is an important step towards a general model of DCF and its derivatives • Though simulations are limited, it provides some degree of validation to the throughput models • It was a worthwhile investigation indeed helping me taking EE384* skills to different areas in networking

  14. Summary & Future Work • Summarized prior art in DCF throughput and hidden station modeling • Developed static and dynamic hidden station models for 802.11 DCF • Developed a finite load throughput model for DCF • Integrated hidden station models for different types of loads • Showed limited simulation and … • Fixed relationships among reachable/hidden stations • Finite load validation with CBR traffic (per group) • Finite load validation with VBR traffic, e.g. Bernoulli IID, exponential, bursty, .. • Scheduling packets in fixed src-dst pairs in multi-channel medium, e.g. iSLIP wireless networks 

  15. Q&ASimulation scripts, code, topologies, traffic pattern files can be found at- http://www.stanford.edu/~sirahman/80211dcf/THANK YOU

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