Q-learning and Markov optimization for spectrum management in cognitive radio networks

Abstract

The paper presents the mechanisms of interaction of Markov models with reinforcement learning algorithms, in particular Q-learning. It describes how cognitive network nodes learn optimal access strategies using rewards for efficient spectrum use and penalties for conflicts with primary users. It also considers the features of a dynamic environment that changes according to the probabilities of transitions between the "busy" and "free" states.