Dynamic Decision Making for Candidate Access Point Selection

Abstract

In this paper, we solve the problem of candidate access point selection in 802.11 networks, when there is more than one access point available to a station. We use the QBSS (quality of service enabled basic service set) Load Element of the new WLAN standard 802.11e as prior information and deploy a decision making algorithm based on reinforcement learning. We show that using reinforcement learning, wireless devices can reach more efficient decisions compared to static methods of decision making which opens the way to a more autonomic communication environment. We also present how the reinforcement learning algorithm reacts to changing situations enabling self adaptation. The authors Burak Simsek and Katinka Wolter would like to thank German Research Foundation (DFG) for support of this work under grant number WO 898/1-2.

The Quest for Self-Model in Self-Managing Networks

“…As described in Sections 2.1 and 2.2, the functionality of ENE is simply based on the desire for having more satisfaction and on the ability to trigger the possessed functionalities of the system in which it is embedded. We refer to our previous study (Simsek et al, 2006b) for the implementation of this model and its results.…”