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

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