Reinforcement Learning based Secondary User Transmissions in Cognitive Radio Networks

Abstract

In this paper, we address the decision making criteria of a secondary user (SU) for deciding whether to transmit or not upon performing spectrum sensing and detecting the presence of any primary user (PU) in the environment in a cognitive radio network (CRN). We propose a reinforcement learning (RL) based approach by a Markov process at the SU node and present novel analytical methods to analyze the performance of such approaches. In particular, we define the probability of interference Pi and the probability of wastage P w, and compare these metrics with a RL based and a non-RL based approach for SU transmission. The simulations show the presence of a tradeoff in the two probability metrics..