Successive Convex Quadratic Programming for Quality-of-Service Management in Full-Duplex MU-MIMO Multicell Networks

Tam, HHM; Tuan, HD; Ngo, DT

Successive Convex Quadratic Programming for Quality-of-Service Management in Full-Duplex MU-MIMO Multicell Networks

Tam, HHM Tuan, HD

Ngo, DT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Communications, 2016, 64 (6), pp. 2340 - 2353
Issue Date:: 2016-06-01

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Field	Value	Language
dc.contributor.author	Tam, HHM	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Ngo, DT	en_US
dc.date.issued	2016-06-01	en_US
dc.identifier.citation	IEEE Transactions on Communications, 2016, 64 (6), pp. 2340 - 2353	en_US
dc.identifier.issn	0090-6778	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122774
dc.description.abstract	© 2016 IEEE. This paper designs jointly optimal linear precoders for both base stations (BSs) and users in a multiuser multi-input multi-output (MU-MIMO) multicell network. The BSs are full-duplexing transceivers while uplink users and downlink users (DLUs) are equipped with multiple antennas. Here, the network quality-of-service (QoS) requirement is expressed in terms of the minimum throughput at the BSs and DLUs. We consider the problems of either QoS-constrained sum throughput maximization or minimum cell throughput maximization. Due to the nonconcavity of the throughput functions, the optimal solutions of these two problems remain unknown in both half-duplexing and full-duplexing networks. The first problem has a nonconcave objective and a nonconvex feasible set, whereas the second problem has a nonconcave and nonsmooth objective. To solve such challenging optimization problems, we develop iterative low-complexity algorithms that only invoke one simple convex quadratic program at each iteration. Since the objective value is proved to iteratively increase, our path-following algorithms converge at least to the local optimum of the original nonconvex problems. Due to their guaranteed convergence, simple implementation, and low complexity, the devised algorithms lend themselves to practical precoder designs for large-scale full-duplex MU-MIMO multicell networks. Numerical results demonstrate the advantages of our successive convex quadratic programming framework over existing solutions.	en_US
dc.relation.ispartof	IEEE Transactions on Communications	en_US
dc.relation.isbasedon	10.1109/TCOMM.2016.2550440	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Successive Convex Quadratic Programming for Quality-of-Service Management in Full-Duplex MU-MIMO Multicell Networks	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	64	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0804 Data Format	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
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	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	64	en_US

Abstract:

© 2016 IEEE. This paper designs jointly optimal linear precoders for both base stations (BSs) and users in a multiuser multi-input multi-output (MU-MIMO) multicell network. The BSs are full-duplexing transceivers while uplink users and downlink users (DLUs) are equipped with multiple antennas. Here, the network quality-of-service (QoS) requirement is expressed in terms of the minimum throughput at the BSs and DLUs. We consider the problems of either QoS-constrained sum throughput maximization or minimum cell throughput maximization. Due to the nonconcavity of the throughput functions, the optimal solutions of these two problems remain unknown in both half-duplexing and full-duplexing networks. The first problem has a nonconcave objective and a nonconvex feasible set, whereas the second problem has a nonconcave and nonsmooth objective. To solve such challenging optimization problems, we develop iterative low-complexity algorithms that only invoke one simple convex quadratic program at each iteration. Since the objective value is proved to iteratively increase, our path-following algorithms converge at least to the local optimum of the original nonconvex problems. Due to their guaranteed convergence, simple implementation, and low complexity, the devised algorithms lend themselves to practical precoder designs for large-scale full-duplex MU-MIMO multicell networks. Numerical results demonstrate the advantages of our successive convex quadratic programming framework over existing solutions.

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http://hdl.handle.net/10453/122774