An intelligent two-agent self-configuration approach for radio resource management

In this paper we propose the use of a two-agent learning scheme for the management of radio resources on cellular access networks. The management is materialized by the implementation of a self-configuration system governing the setup of several parameters on each base station. The two agents have independent goals; one is trying to maximize the quality of service and the other the economic benefit. Thanks to the combined use of the fuzzy logic technique and reinforcement learning, both agents will work in a complementary mode, achieving both goals simultaneously.