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A cooperative spectrum sensing with multi-agent reinforcement learning approach in cognitive radio networks

A cooperative spectrum sensing with multi-agent reinforcement learning approach in cognitive radio networks
A cooperative spectrum sensing with multi-agent reinforcement learning approach in cognitive radio networks

Cognitive radio networks (CRNs) can greatly improve the temporal and spatial spectrum utilization by identifying and exploring spectrum holes of the licensed primary users (PUs). However, since the occupation of primary channels changes dynamically, a swift and accurate spectrum sensing is crucial especially in the multi-channel multi-secondary users (SUs) environment, where the number of channels is much larger than that of SUs. To improve the sensing accuracy, a cooperative sensing algorithm is proposed in this letter, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This letter further employs multi-agent deep deterministic policy gradient (MADDPG) algorithm with the feature of centralized training and decentralized execution to reduce the synchronization and communication overhead caused by the sensing cooperation of SUs. The numerical simulation demonstrates that with the combination of cooperative sensing and multi-agent reinforcement learning, the proposed algorithm can greatly enhance the sensing accuracy in comparison to other non-cooperative learning or centralized learning approaches.

Cognitive Radio Networks, Cooperative Spectrum Sensing, Deep Reinforcement Learning, Fading channels, Multi-Agent Deep Deterministic Policy Gradient, Reinforcement learning, Reliability, Sensors, Shadow mapping, Synchronization, Training
1089-7798
2604-2608
Gao, Ang
c50ad415-a1c3-4592-beee-b1ff254eb087
Du, Chengyuan
c012ebfb-4b91-4a09-af00-4b9b47ef9ec2
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Liang, Wei
cd85f9fc-1c3b-4ecd-bef6-3e5f31f18ef5
Gao, Ang
c50ad415-a1c3-4592-beee-b1ff254eb087
Du, Chengyuan
c012ebfb-4b91-4a09-af00-4b9b47ef9ec2
Ng, Soon Xin
e19a63b0-0f12-4591-ab5f-554820d5f78c
Liang, Wei
cd85f9fc-1c3b-4ecd-bef6-3e5f31f18ef5

Gao, Ang, Du, Chengyuan, Ng, Soon Xin and Liang, Wei (2021) A cooperative spectrum sensing with multi-agent reinforcement learning approach in cognitive radio networks. IEEE Communications Letters, 25 (8), 2604-2608, [9426930]. (doi:10.1109/LCOMM.2021.3078442).

Record type: Article

Abstract

Cognitive radio networks (CRNs) can greatly improve the temporal and spatial spectrum utilization by identifying and exploring spectrum holes of the licensed primary users (PUs). However, since the occupation of primary channels changes dynamically, a swift and accurate spectrum sensing is crucial especially in the multi-channel multi-secondary users (SUs) environment, where the number of channels is much larger than that of SUs. To improve the sensing accuracy, a cooperative sensing algorithm is proposed in this letter, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This letter further employs multi-agent deep deterministic policy gradient (MADDPG) algorithm with the feature of centralized training and decentralized execution to reduce the synchronization and communication overhead caused by the sensing cooperation of SUs. The numerical simulation demonstrates that with the combination of cooperative sensing and multi-agent reinforcement learning, the proposed algorithm can greatly enhance the sensing accuracy in comparison to other non-cooperative learning or centralized learning approaches.

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A Cooperative Spectrum Sensing with Multi-Agent Reinforcement Learning Approach in Cognitive Radio Networks - Accepted Manuscript
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Accepted/In Press date: 5 May 2021
e-pub ahead of print date: 10 May 2021
Published date: August 2021
Additional Information: Funding Information: Manuscript received April 18, 2021; accepted May 5, 2021. Date of publication May 10, 2021; date of current version August 12, 2021. The work was supported by China and Shaanxi Postdoctoral Science Foundation 2017M623243, 2018BSHYDZZ26, Shaanxi and Guangxi Keypoint Research and Invention Program 2019ZDLGY13-02-02, AB19110036, and Taicang Keypoint Science and Technology Plan TC2018SF03, TC2019SF03. The associate editor coordinating the review of this letter and approving it for publication was G. Brante. (Corresponding author: Wei Liang.) Ang Gao, Chengyuan Du, and Wei Liang are with the School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China (e-mail: liangwei@nwpu.edu.cn). Publisher Copyright: © 1997-2012 IEEE.
Keywords: Cognitive Radio Networks, Cooperative Spectrum Sensing, Deep Reinforcement Learning, Fading channels, Multi-Agent Deep Deterministic Policy Gradient, Reinforcement learning, Reliability, Sensors, Shadow mapping, Synchronization, Training

Identifiers

Local EPrints ID: 449725
URI: http://eprints.soton.ac.uk/id/eprint/449725
ISSN: 1089-7798
PURE UUID: 8da6b395-a8c0-48ba-8b92-c4d6b12e2a4f
ORCID for Soon Xin Ng: ORCID iD orcid.org/0000-0002-0930-7194

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Date deposited: 14 Jun 2021 16:31
Last modified: 15 Jun 2022 01:34

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Contributors

Author: Ang Gao
Author: Chengyuan Du
Author: Soon Xin Ng ORCID iD
Author: Wei Liang

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