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

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...

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Veröffentlicht in:IEEE transactions on communications Jg. 64; H. 6; S. 2340 - 2353
Hauptverfasser: Tam, Ho Huu Minh, Tuan, Hoang Duong, Ngo, Duy Trong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Antennas
Convergence
Downlink
Full-duplexing transceiver
Interference
Iterative methods
Mathematical models
Maximization
multicell network
Networks
Optimization
precoder design
Quadratic programming
Quality of service
quality-of-service (QoS) management
successive convex quadratic programming
Throughput
Uplink
Full-duplexing transceiver
successive convex quadratic programming
multicell network
precoder design
quality-of-service (QoS) management
ISSN:0090-6778, 1558-0857
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Zusammenfassung: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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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2016.2550440