dynamic tuning of the ieee 802.11 protocol to achieve a theoretical throughput limit frederico...

Dynamic Tuning of the IEEE 802.11 Protocol to Achieve a Theoretical Throughput Limit

Frederico Calì, Marco Conti, and Enrico GregoriIEEE/ACM TRANSACTIONS ON NETWORKING, VOL. 8, NO. 6, DECEMBER 2000

Overview

Introduction IEEE 802.11 Capacity Analysis IEEE 802.11+ Protocol Comments Conclusions

Introduction – (1)

The existing IEEE 802.11 protocol is contention-based, which allows stations to access the wireless channel randomly.

Its performance degrades under heavy network load due to the high collision probability.

Introduction – (2)

Motivation: find the optimal contention window (CWIN) size which boosts up the throughput.The value of CWIN which is large enough to

minimize the collision probability and small enough to minimize the backoff interval.

Introduction – (3) An analytical model is built to derive the average size

of the contention window which maximizes the throughput.

Observations: Depending on the network configuration, the standard can o

perate very far from the theoretical throughput limit. An appropriate tuning of the backoff algorithm can drive the

IEEE 802.11 protocol close to the theoretical throughput limit.

Introduction – (4) Hence a distributed algorithm IEEE802.11+ is pr

oposed that enables each station to tune its backoff algorithm at run-time.

IEEE 802.11 Capacity Analysis (1) No RTS/CTS mechanism No dependency on the physical layer

technology WLAN configuration

A model with a finite number of stations (M) which always have a packet ready for transmission.

For each transmission attempt, a station is assumed to use a backoff interval sampled from a geometric distribution with parameter p, where

IEEE 802.11 Capacity Analysis (2)

In the real IEEE 802.11 backoff algorithm, a station transmission probability depends on the history.

However, our model of the protocol behavior provides accurate estimates of the IEEE 802.11 protocol behavior from a capacity analysis standpoint.


Protocol Capacity (Max throughput)

tv = average virtual transmission time

m- = average message length


……..(1)

S = time required to complete a successful transmission (DATA-ACK frame exchange without collision)


Derivation of the average message length



Derivation of E[B]:

Corollary:

Given Lemma 2

Derivation of the average tv


……..(2)

The assumption on the backoff algorithm implies that the future behavior of a station does not depend on the past.

The idle period times are i.i.d. sampled from a geometric distribution with an average .

The collision lengths are i.i.d with average .



……..(3)

From (1), (3) and Lemma 3


……..(4)

Validate the analytical model via simulation.

Given M and q, how close are the throughput derived analytically and that derived from simulation ?


Given M and q, find p which maximizes the capacity in (4).M = the number of stationsp = a parameter for the geometric distribution

of the backoff timeq = a parameter for the geometric distribution

of the packet length (number of slots)



For a given packet length distribution, the maximum value of the capacity corresponds to the minimum value of the average virtual transmission time (tv).


Goal: Find p (pmin) to minimize tv:

Motivation

Based on the simulation, the IEEE 802.11 protocol with an appropriate setting of the CWIN size (optimal CWIN) can reach the theoretical limit.

IEEE 802.11+ (1)

IEEE 802.11+ (2)

Tune the value of E[CWIN] based on pmin at run time such that the capacity is maximized.

Both E[Coll] and E[Nc] can be estimated by observing the channel status.

A station can obtain pmin with a minimization algorithm.

IEEE 802.11+ (3)

The minimization algorithm is NOT suitable for a run-time computation as it very complex computationally.

A heuristic is introduced to approximate pm

in.

IEEE 802.11+ (4)

Approximate pmin by finding p satisfying

The selection of pmin guarantees that E[Coll] < 1.

IEEE 802.11+ (5)

IEEE 802.11+ (6)

IEEE 802.11+ (7)

The contention window size is updated at the end of any virtual transmission time that contains at least one collision.

IEEE 802.11+ (8)

IEEE 802.11+ (9)

The previous results were obtained under the following assumptions:M (the number of stations) is known before.No hidden terminals

Study how IEEE 802.11+ is sensitive to the number of active stationsHidden terminals

IEEE 802.11+ (10)

Knowing M beforehand is a strong assumption. In a real network, the number of active

stations is highly variable.M cannot be more or less the same all the

time

Relax this assumption and analyze the sensitiveness of the IEEE802.11+ capacity to the number of active stations.

IEEE 802.11+ (11)

IEEE 802.11+ (12)Assume M=100

The number of active stations (M) should be estimated at run time.

M can be computed provided that the average number of empty slots in a virtual transmission time is known.

IEEE 802.11+ (13)

IEEE 802.11+ (14)

To avoid sharp changes in the estimated value of M, is introduced

IEEE 802.11+ (15)

IEEE 802.11+ (16)

IEEE 802.11+ (17)

10010 10

IEEE 802.11+ (18)

Relax the 2nd assumption – Hidden stations exist. Study how hidden stations affect the performance of our

protocol by causing erroneous statistics.

Analyze the impact on our protocol of three events that may occur when hidden stations are present. missed ACK, carrier sensing fault and not-detected transmission

IEEE 802.11+ (19) – Hidden stations

Missed ACK The hidden station problem may cause a station

to miss the ACK.

ConsequencesObserves a longer virtual transmission timeConsiders a successful transmission to be a

collision



Missed ACK

Carrier Sensing Fault A station (1) wrongly senses the wireless

medium has been idle while a hidden station is transmitting and (2) starts transmitting.

ConsequencesGenerate collisions


Non-detected Transmission A station cannot observe some collisions and su

ccessful transmissions and mistakenly interpret that the channel is idle.

Consequences Overestimate the idle-period length. Underestimate the number of collision and the collisio

n length.


A probabilistic model is used to associate each phenomenon to a probability

H1: the probability that a station misses an ACK.

H2: the probability that, due to a carrier sensing fault, a station which does not detect an ongoing transmission starts transmitting.

H3: the probability that a station overestimate the idle-period length.



(50 active stations)

Given H1=H2=H3=H

Good

Make valid assumptions - the geometric distribution of backoff time E[B].Justified by comparing figures derived from th

e simulation and the mathematical analysis.

Demonstrate how to handle the big assumption step by step.

Comments – (1)

Bad

The overhead incurred for estimating pmin is not shown. It won’t appear if IEEE 802.11+ is simulated in ns-2.

Comments – (2)

Conclusions

A distributed algorithm IEEE802.11+ is proposed that enables each station to tune its backoff algorithm at run-time.

The throughput can be optimized such that it is close to the theoretical upper bound.

dynamic tuning of the ieee 802.11 protocol to achieve a theoretical throughput limit frederico...

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