Aggregated interference control for cognitive radio networks based on multi-agent learning

This paper deals with the problem of aggregated interference generated by multiple cognitive radios (CR) at the receivers of primary (licensed) users. In particular, we consider a secondary CR system based on the IEEE 802.22 standard for wireless regional area networks (WRAN), and we model it as a multi-agent system where the multiple agents are the different secondary base stations in charge of controlling the different secondary cells. We propose a solution for the aggregated interference problem based on a form of real-time multi-agent reinforcement learning known as decentralized Q-learning, so that the multi-agent system is designed to learn an optimal policy by directly interacting with the surrounding environment in a distributed fashion. Simulation results reveal that the proposed approach is able to fulfil the primary users interference constraints, without introducing signalling overhead in the system.