Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning

Abstract

Dynamic spectrum access (DSA) has been considered as a promising technology to address spectrum scarcity and improve spectrum utilization. Normally, the channels are related to each other. Meanwhile, collisions will be inevitably caused by communicating between multiple PUs or multiple SUs in a real DSA environment. Considering these factors, the deep multi-user reinforcement learning (DMRL) is proposed by introducing the cooperative strategy into dueling deep Q network (DDQN). With no demand of prior information about the system dynamics, DDQN can efficiently learn the correlations between channels, and reduce the computational complexity in the large state space of the multi-user environment. To reduce the conflicts and further maximize the network utility, cooperative channel strategy is explored by utilizing the acknowledge (ACK) signals without exchanging spectrum information. In each time slot, each user selects a channel and transmits a packet with a certain probability. After sending, ACK signals are utilized to judge whether the transmission is successful or not. Compared with other popular models, the simulation results show that the proposed DMRL can achieve better performance on effectively enhancing spectrum utilization and reducing conflict rate in the dynamic cooperative spectrum sensing.