On suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks

Abstract

An ever growing number of deployed wireless networks dictates a tempo with which the inter-network cooperation techniques are being developed. Cooperation, in this sense, can go far beyond a simple activation of an interference avoidance techniques. This paper describes and evaluates the performance of a reinforcement learning based reasoning engine, used in a self-learning, cognitively controlled cooperation between heterogeneous, co-located networks. Coupled with a concept of cooperation through the network service negotiation, this approach represents an efficient, yet scalable solution for the dynamic network self-optimization. © 2013 Springer-Verlag.