Joint user selection and power allocation optimization for energy-efficient MU-MIMO systems with limited feedback

Bonsu, KA; Pan, S; Ansere, JA; Zhou, W

Joint user selection and power allocation optimization for energy-efficient MU-MIMO systems with limited feedback

Bonsu, KA Pan, S Ansere, JA Zhou, W

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Telecommunication Systems, 2021, 77, (3), pp. 479-492
Issue Date:: 2021-07-01

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Field	Value	Language
dc.contributor.author	Bonsu, KA
dc.contributor.author	Pan, S
dc.contributor.author	Ansere, JA
dc.contributor.author	Zhou, W https://orcid.org/0000-0002-1282-853X
dc.date.accessioned	2022-05-07T23:18:04Z
dc.date.available	2022-05-07T23:18:04Z
dc.date.issued	2021-07-01
dc.identifier.citation	Telecommunication Systems, 2021, 77, (3), pp. 479-492
dc.identifier.issn	1018-4864
dc.identifier.issn	1572-9451
dc.identifier.uri	http://hdl.handle.net/10453/157114
dc.description.abstract	The multi-user multiple-input multiple-output (MU-MIMO) systems can greatly improve the system throughput. An efficient user selection and power allocation schemes are essential to achieve the needed performance. This paper examines energy efficiency maximization under limited feedback in MU-MIMO systems. An expression for the achievable data rate is derived for RRH-user selection which is used to obtain the optimization framework for the energy efficiency (EE). The system EE is optimized through power allocation, RRH-user selection and number of antennas adjustment when the quality of service requirements and maximum transmit power constraints are satisfied. The formulated problem is NP-hard and non-convex. Based on Lagrange dual decomposition and successive convex approximation, a practical scheme is proposed to tackle the problem. It can be observed that there is an improvement of 4% in the energy efficiency of the proposed algorithm as compared with the energy efficiency of the existing algorithm. Furthermore, the computational complexity of the proposed algorithm is discussed. Finally, the proposed algorithm is validated through simulations and the results show that the proposed algorithm outperforms the existing algorithms in terms of the system EE.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Telecommunication Systems
dc.relation.isbasedon	10.1007/s11235-021-00765-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Joint user selection and power allocation optimization for energy-efficient MU-MIMO systems with limited feedback
dc.type	Journal Article
utslib.citation.volume	77
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	2022-05-07T23:18:01Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	77
utslib.citation.issue	3

Abstract:

The multi-user multiple-input multiple-output (MU-MIMO) systems can greatly improve the system throughput. An efficient user selection and power allocation schemes are essential to achieve the needed performance. This paper examines energy efficiency maximization under limited feedback in MU-MIMO systems. An expression for the achievable data rate is derived for RRH-user selection which is used to obtain the optimization framework for the energy efficiency (EE). The system EE is optimized through power allocation, RRH-user selection and number of antennas adjustment when the quality of service requirements and maximum transmit power constraints are satisfied. The formulated problem is NP-hard and non-convex. Based on Lagrange dual decomposition and successive convex approximation, a practical scheme is proposed to tackle the problem. It can be observed that there is an improvement of 4% in the energy efficiency of the proposed algorithm as compared with the energy efficiency of the existing algorithm. Furthermore, the computational complexity of the proposed algorithm is discussed. Finally, the proposed algorithm is validated through simulations and the results show that the proposed algorithm outperforms the existing algorithms in terms of the system EE.

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http://hdl.handle.net/10453/157114