Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks

Abstract

The adoption of the Fifth Generation (5G) and beyond 5G networks is driving the demand for learning approaches that enable users to co-exist harmoniously in a multi-user distributed environment. Although resourceconstrained, the Cognitive Radio (CR) has been identified as a key enabler of distributed 5G and beyond networks due to its cognitive abilities and ability to access idle spectrum opportunistically. Reinforcement learning is well suited to meet the demand for learning in 5G and beyond 5G networks because it does not require the learning agent to have prior information about the environment in which it operates. Intuitively, CRs should be enabled to implement reinforcement learning to efficiently gain opportunistic access to spectrum and co-exist with each other. However, the application of reinforcement learning is straightforward in a single-agent environment and complex and resource intensive in a multi-agent and multi-objective learning environment. In this paper, (1) we present a brief history and overview of reinforcement learning and its limitations; (2) we provide a review of recent multi-agent learning methods proposed and multi-agent learning algorithms applied in Cognitive Radio (CR) networks; and (3) we further present a novel framework for multi-CR reinforcement learning and conclude with a synopsis of future research directions and recommendations.

Interference Situational Aware Beam Pointing Optimization for Dense LEO Satellite Communication System

“…CR is also an effective technique to mitigate co-sharing spectrum interference [9]. It can sense the available frequency band in the spectrum for use, which can improve spectrum efficiency and reduce the mutual interference between systems [10][11][12]. Zhang et al have proposed a spectrum strategy to distinguish the GEO signal from the interfering NGEO and noise [13], and the GEO satellite can use the appropriate power to protect its normal communication.…”