Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach

Wang, S; Lv, T; Ni, W; Beaulieu, NC; Guo, YJ

Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach

Wang, S Lv, T Ni, W

Beaulieu, NC Guo, YJ

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2021, 20, (9), pp. 5672-5688
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Lv, T
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Beaulieu, NC
dc.contributor.author	Guo, YJ
dc.date.accessioned	2022-03-18T02:59:21Z
dc.date.available	2022-03-18T02:59:21Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2021, 20, (9), pp. 5672-5688
dc.identifier.issn	1536-1276
dc.identifier.issn	1558-2248
dc.identifier.uri	http://hdl.handle.net/10453/155309
dc.description.abstract	This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Wireless Communications
dc.relation.isbasedon	10.1109/twc.2021.3069240
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach
dc.type	Journal Article
utslib.citation.volume	20
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-18T02:59:18Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	9

Abstract:

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.

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http://hdl.handle.net/10453/155309