Applications of Reinforcement Learning to Cognitive Radio Networks

Abstract

Cognitive Radio (CR) enables an unlicensed user to change its transmission and reception parameters adaptively according to spectrum availability in a wide range of licensed channels. The concept of a Cognition Cycle (CC) is the key element of CR to provide context awareness and intelligence so that each unlicensed user is able to observe and carry out an optimal action on its operating environment for performance enhancement. The CC can be applied in various application schemes in CR networks such as Dynamic Channel Selection (DCS), topology management, congestion control, and scheduling. In this paper, Reinforcement Learning (RL) is applied to implement the conceptual of the CC. We provide an extensive overview of our work including single-agent and multi-agent approaches to show that RL is a promising technique. Our contribution in this paper is to propose various application schemes using our RL approach to warrant further research on RL in CR networks.