Performance Analysis of Reinforcement Learning for Achieving Context Awareness and Intelligence in Mobile Cognitive Radio Networks

Abstract

Cognitive Radio (CR) is a key technology for improving the utilization level of the overall radio spectrum in wireless communications. It is able to sense and change its transmission and reception parameters adaptively according to spectrum availability in different spectrum channels. The Cognition Cycle (CC) is a state machine that is embodied in each CR host that defines the mechanisms related to achieving context awareness and intelligence including observation, learning, and action selection. The CC is the key element in the design of various applications in CR networks such as Dynamic Channel Selection (DCS), scheduling and congestion control. In this paper, Reinforcement Learning (RL) is employed to implement the CC in mobile CR networks. Previous works consider static networks with homogeneous channels. This paper analyzes the performance of RL as an approach to achieve context awareness and intelligence in regard to DCS in mobile CR networks with heterogeneous channels. Our contribution in this paper is to show whether RL is an appropriate tool to implement the CC. The results presented in this paper show that RL is a promising approach.