Green opportunistic access for cognitive radio networks: A regret matching based approach

Abstract

In cognitive radio networks, the spectrum sensing is extremely important. Although this is simple with a very short detection time, the competitive behavior of secondary users can lead to additional expenses in terms of energy. Indeed, the contention on data channel unoccupied by licensed user leads to a single winner, but also involves a loss of energy of all nodes. In this paper, we investigate the problem of energy efficient spectrum sensing, by applying an approach based on regret-matching learning, to improves secondary users battery life and the performance of the network. We will demonstrate by simulations, that the regret matching learning can achieve good results.