Reinforcement Learning based Secondary User Transmissions in Cognitive Radio Networks

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 P-i 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 P-w and P-i, based on the Markov process. The simulation results are compared in the form of the transmitter operating characteristics (ToC) curves. Using our approach, one could control the interference to the PU by trading off with the spectral wastage.