On Suitability of the Reinforcement Learning Methodology in Dynamic, Heterogeneous, Self-optimizing Networks

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 selflearning, 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 selfoptimization.