A Distributed Q Learning Spectrum Decision Scheme for Cognitive Radio Sensor Network

Abstract

Cognitive spectrum management can improve the utilization efficiency of spectrum while increasing the energy consumption of sensor network nodes. Hence, how to balance the energy consumption and spectrum efficiency has become a critical challenge in the resource-constrained cognitive radio sensor networks. In this paper, by analyzing the channel characteristics and the energy efficiency of networks, a joint channel selection and power control spectrum decision algorithm based on distributed Q learning is proposed. To evaluate the performance of the proposed framework, an optimal Q value subject to communication efficiency index is formulated. Then, the learning strategy selection scheme is designed to solve the optimization problem by establishing a learning model. In this learning model, each node can get the strategy of other nodes to select the optimal strategy by introducing distributed strategy estimation. The simulation results show that the proposed algorithm has better performance than the existing methods.