By Topic

A Distributed Access Point Selection Algorithm Based on No-Regret Learning for Wireless Access Networks

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
$33 $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

1 Author(s)
Lin Chen ; LRI, Univ. of Paris-Sud XI, Orsay, France

The proliferation of wireless access technologies offers users the possibility of choosing among multiple available wireless access networks to connect to. This paper focuses on such network selection problem in the context of IEEE 802.11 WLANs where several access points provide connection service to users. We formulate this problem as a non-cooperative game where each user tries to maximize its utility function, defined as the throughput reward minus the fee charged by the access point. We then conduct a systematic analysis on the formulated game and develop an access point selection algorithm based on no-regret learning to orient the system converges to an equilibrium state (correlated equilibrium). The proposed algorithm, which can be implemented distributedly based on local observation, is especially suited in decentralized adaptive learning environments as wireless access networks. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm in achieving high system efficiency.

Published in:

Vehicular Technology Conference (VTC 2010-Spring), 2010 IEEE 71st

Date of Conference:

16-19 May 2010