By Topic

A distributed dynamic channel allocation technique for throughput improvement in a dense WLAN environment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hui Luo ; AT&T Labs, Middletown, NJ, USA ; Shankaranarayanan, N.K.

In a dense WLAN environment, the signal coverage area of each access point (AP) typically has significant overlap with that of the neighboring APs. This is a problem if there are limited frequency channels. This paper presents an algorithm that can improve per-user throughput significantly, particularly for nonuniform traffic conditions. It is based on a cellular neural network model. Like a cellular neuron changing its state, based on the information of its neighboring neurons, every AP determines the best channel it should use in the next time slot, based solely on the traffic load of its neighboring APs and the channels used by them in the current time slot, but it actually switches to that channel with some fixed probability less than one. All APs in the network perform the above operation simultaneously. Computer simulations show that (1) given any traffic load distribution and any initial channel allocation, the algorithm converges to an equilibrium state in a short time, in which the overall throughput of the network is significantly improved; and (2) there exists an optimal switching probability that can minimize the time for the algorithm to reach the equilibrium state. The proposed technique has significant practical value due to its simplicity and effectiveness.

Published in:

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on  (Volume:5 )

Date of Conference:

17-21 May 2004