Decentralized Q-Learning for Aggregated Interference Control in Completely and Partially Observable Cognitive Radio Networks
Ana Galindo‐Serrano, Lorenza Giupponi · 2010
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 secondary cells. We propose a form of real-time multi-agent reinforcement learning, known as decentralized Q-learning, to manage the aggregated interference generated by multiple WRAN cells. We consider both situations of complete and partial information about the environment. By directly interacting with the surrounding environment in a distributed fashion, the multi-agent system is able to learn, in the first case, an optimal policy to solve the problem and, in the second case, a reasonably good suboptimal policy. Simulation results reveal that the proposed approach is able to fulfill the primary users interference constraints, without introducing signaling overhead in the system.