Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach

Wang, Z; Lin, Z; Lv, T; Ni, W

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach

Wang, Z Lin, Z

Lv, T Ni, W

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2021, 8, (18), pp. 14232-14247
Issue Date:: 2021-09-15

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Field	Value	Language
dc.contributor.author	Wang, Z
dc.contributor.author	Lin, Z https://orcid.org/0000-0001-5941-2163
dc.contributor.author	Lv, T
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.date.accessioned	2022-06-08T01:53:13Z
dc.date.available	2022-06-08T01:53:13Z
dc.date.issued	2021-09-15
dc.identifier.citation	IEEE Internet of Things Journal, 2021, 8, (18), pp. 14232-14247
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/158011
dc.description.abstract	In multicell massive multiple-input-multiple-output (MIMO) nonorthogonal multiple access (NOMA) networks, base stations with multiple antennas deliver their radio-frequency energy in the downlink, and Internet-of-Things devices use their harvested energy to support uplink data transmission. This article investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer. To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information are considered and their corresponding EE performance is analyzed. Under the Quality-of-Service requirements, an EE maximization problem is formulated, which is nontrivial due to nonconvexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex and then develop a distributed alternating direction method of multipliers-based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2021.3068721
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks with Wireless Power Transfer: A Distributed ADMM Approach
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0805 Distributed Computing
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-08T01:52:57Z
pubs.issue	18
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	18

Abstract:

In multicell massive multiple-input-multiple-output (MIMO) nonorthogonal multiple access (NOMA) networks, base stations with multiple antennas deliver their radio-frequency energy in the downlink, and Internet-of-Things devices use their harvested energy to support uplink data transmission. This article investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer. To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information are considered and their corresponding EE performance is analyzed. Under the Quality-of-Service requirements, an EE maximization problem is formulated, which is nontrivial due to nonconvexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex and then develop a distributed alternating direction method of multipliers-based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.

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http://hdl.handle.net/10453/158011