Dynamic Spectrum Sharing Based on Deep Reinforcement Learning in Mobile Communication Systems

Abstract

The rapid development of mobile communication services in recent years has resulted in a scarcity of spectrum resources. This paper addresses the problem of multi-dimensional resource allocation in cognitive radio systems. Deep reinforcement learning (DRL) combines deep learning and reinforcement learning to enable agents to solve complex problems. In this study, we propose a training approach based on DRL to design a strategy for secondary users in the communication system to share the spectrum and control their transmission power. The neural networks are constructed using the Deep Q-Network and Deep Recurrent Q-Network structures. The results of the conducted simulation experiments demonstrate that the proposed method can effectively improve the user’s reward and reduce collisions. In terms of reward, the proposed method outperforms opportunistic multichannel ALOHA by about 10% and about 30% for the single SU scenario and the multi-SU scenario, respectively. Furthermore, we explore the complexity of the algorithm and the influence of parameters in the DRL algorithm on the training.

An Efficient Cooperative Spectrum Sensing for Cognitive Wireless Sensor Networks

“…The rapid growth of industrial electronic equipment and the widespread use of wireless sensors, and its corresponding communication devices and services [1]- [2], has led to a significant increase in demand for spectrum. However, the conventional fixed spectrum allocation policy has restricted the availability of spectrum resources as the majority of it has been assigned to specific services or licensed/primary users (PUs) [3].…”