THROUGHPUT ANALYSIS OF IEEE 802.11 DCF BASIC IN PRESENCE

OF HIDDEN STATIONS

Shahriar Rahman

Stanford Electrical

Engineering

http://ee.stanford.edu

Outline of Talk802.11 DCF Protocol OverviewProblem with DCF Basic AccessModeling Hidden StationsDCF Throughput ModelsSimulation ResultsDiscussions & ConclusionFuture WorkQ&A

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.

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???

A B C D

Saturation Throughput Model

Bianchi provides a saturation throughput model based on a Markov model of backoff mechanism-

Psuccess E[P]

Pidle + Psuccess Ts + Pcollision Tc

Pidle = 1- Ptr and Psuccess = Ptr Ps

Pcollision = Ptr (1 - Ps)

Ptr = 1 – (1 – ) n and Ps = n(1 – ) n-1 /Ptr

Ts and Tc measures time durations of a successful transmission and collided transmission

S =

Hidden Station Model - Static

Kleinrock and Tobagi’s hearing graph-

1 1 1 0 0 12 1 1 0 0 13 0 0 1 1 14 0 0 1 1 05 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) = (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)

Hidden Station Model - Dynamic

Extend static model and allow transitions between k states, over n stations? => adjacency graph

Pr(reachable->reachable) => use control parameter,

Pr(hidden->*) = 1/l, Pr(reachable->hidden) = (1-)/(l-1)

Balance equations: Pr(j) + (1 – l) Ph(j) = 1

(1 - )/(1 - l) Pr(j) = (1/l) Ph(j)

Solve to get: Pr(j) = 1 / (1 + l(1 – ))

1

2 k1

2

3

4

k-state Markov chain

Adjacencygraph

Our Throughput Model - SaturationWorst case throughput

loss => hidden stations always transmit

Ptr = 1; Ps = Nre(1 – ) 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

.10 .30 .50 .70 .90

1.0

.80

.60

.40

.20

.00

n = 50n = 20n = 10n = 5

Probability of hidden stations

Norm

alized throughput

Our Throughput Model – Finite Load(1)

Similar grouping into k groups, but now with

identical loads, i individually and i = per 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 Ps and Ptr into throughput equation Can be used for both basic and RTS/CTS

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 Ps and 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 . One idea may be

from scheduler’s history at stationsCertainly justifies RTS/CTS, MACAW, DCF+, etc.

Simulation Topology & Traffic

1

2

3

4

5

<=250m

>250m

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

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

Saturation Simulation ResultsSimulated 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 and simulate finite load throughput

.10 .30 .50 .70 .90

1.0

.80

.60

.40

.20

.00

Probability of hidden stations

Norm

alized throughput

n=50

n=20

n=10

n=5

Discussions & ConclusionHidden 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 modelProposed 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

Summary & Future WorkSummarized 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

Q&A

Simulation scripts, code, topologies, traffic pattern files can be found at-

http://www.stanford.edu/~sirahman/80211dcf/

THANK YOU