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Open Access

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

School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 0001, South Africa
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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 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.

Keywords

cognitive radiodeep reinforcement learningmulti-agent reinforcement learningmean field reinforcement learningorganic computing

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Intelligent and Converged Networks
Volume 4 Issue 1,
March 2023
Pages 50-75
DOI: 10.23919/ICN.2023.0005
Cite this article:
Rapetswa K, Cheng L. Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks. Intelligent and Converged Networks, 2023, 4(1): 50-75. https://doi.org/10.23919/ICN.2023.0005

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Received: 09 February 2023
Accepted: 31 March 2023
Published: 20 March 2023
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/
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