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International Journal of Antennas and Propagation
Volume 2017, Article ID 4302950, 9 pages
Research Article

Joint Antenna Selection and Precoding Optimization for Small-Cell Network with Minimum Power Consumption

1The School of Electronic and Information, Nantong University, Nantong 226019, China
2The National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
3The Nantong Research Institute for Advanced Communication Technologies, Nantong 226019, China

Correspondence should be addressed to Chen Xu; nc.ude.utn@nehcux

Received 7 July 2016; Accepted 15 March 2017; Published 28 March 2017

Academic Editor: Sotirios K. Goudos

Copyright © 2017 Qiang Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We focus on the power consumption problem for a downlink multiuser small-cell network (SCN) considering both the quality of service (QoS) and power constraints. First based on a practical power consumption model taking into account both the dynamic transmit power and static circuit power, we formulate and then transform the power consumption optimization problem into a convex problem by using semidefinite relaxation (SDR) technique and obtain the optimal solution by the CVX tool. We further note that the SDR-based solution becomes infeasible for realistic implementation due to its heavy backhaul burden and computational complexity. To this end, we propose an alternative suboptimal algorithm which has low implementation overhead and complexity, based on minimum mean square error (MMSE) precoding. Furthermore, we propose a distributed correlation-based antenna selection (DCAS) algorithm combining with our optimization algorithms to reduce the static circuit power consumption for the SCN. Finally, simulation results demonstrate that our proposed suboptimal algorithm is very effective on power consumption minimization, with significantly reduced backhaul burden and computational complexity. Moreover, we show that our optimization algorithms with DCAS have less power consumption than the other benchmark algorithms.