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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 paper, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This paper 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
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. (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 paper, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This paper 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
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

Catalogue record

Date deposited: 14 Jun 2021 16:31
Last modified: 17 Jun 2021 01:35

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Contributors

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

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