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

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 resource-constrained,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.

Convergence of mobile broadband and broadcast services:A cognitive radio sensing and sharing perspective

With next generation networks driving the confluence of multi-media,broadband,and broadcast services,Cognitive Radio(CR)networks are positioned as a preferred paradigm to address spectrum capacity challenges.CRs address these issues through dynamic spectrum access.However,the main challenges faced by the CR pertain to achieving spectrum efficiency.As a result,spectrum efficiency improvement models based on spectrum sensing and sharing models have attracted a lot of research attention in recent years,including CR learning models,network densification architectures,and massive Multiple Input Multiple Output(MIMO),and beamforming techniques.This paper provides a survey of recent CR spectrum efficiency improvement models and techniques,developed to support ultra-reliable low latency communications that are resilient to surges in traffic and competition for spectrum.These models and techniques,broadly speaking,enable a wide range of functionality ranging from enhanced mobile broadband to large scale Internet of Things(IoT)type communications.In addition and given the strong correlation between the typical size of a spectrum block and the achievable data rate,the models studied in this paper are applicable in ultra-high frequency band.This study therefore provides a good review of CRs and direction for future investigations into newly identified 5G research areas,applicable in industry and in academia.