Energy-Efficient Massive MIMO With Decentralized Precoder Design

This paper presents an energy-efficient downlink precoding scheme in a multi-cell Massive MIMO system. We approach the precoder design problem to maximize the system energy efficiency by jointly considering power control, interference management, antenna switching and user throughput in a cluster of...

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Published in:	IEEE transactions on vehicular technology Vol. 69; no. 12; pp. 15370 - 15384
Main Authors:	Zhang, Shuai, Yin, Bo, Cheng, Yu, Cai, Lin X., Zhou, Sheng, Niu, Zhisheng, Shan, Hangguan
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Antennas beamforming Computational geometry Computer architecture Convergence Convexity Design parameters distributed optimization Energy conversion efficiency Energy efficiency Massive MIMO Microprocessors MIMO (control systems) Optimization Power control Power management Precoding Quadratic programming Stations Systems design Throughput
ISSN:	0018-9545, 1939-9359
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Abstract	This paper presents an energy-efficient downlink precoding scheme in a multi-cell Massive MIMO system. We approach the precoder design problem to maximize the system energy efficiency by jointly considering power control, interference management, antenna switching and user throughput in a cluster of base stations. This is computationally difficult as it requires solving a sparsity-inducing non-convex optimization problem, which is NP-hard. To alleviate the solution complexity, first a stochastic smooth approximation of zero-norm is applied in the antenna power management to enable fast, gradient-based algorithms. For efficient convergence, we develop a novel optimization algorithm combining augmented multiplier (AM) and quadratic programming (QP), and show how this scheme permits decentralized implementation by offloading parts of the computation to the individual base stations to reduce communication overhead. We provide theoretical proof that the proposed algorithm converges both locally and globally under realistic assumptions. Numerical results confirm that our method achieves higher energy efficiency with a superior convergence rate compared to different types of existing methods, and illustrate the relationship between energy efficiency performance and system design parameters.
AbstractList	This paper presents an energy-efficient downlink precoding scheme in a multi-cell Massive MIMO system. We approach the precoder design problem to maximize the system energy efficiency by jointly considering power control, interference management, antenna switching and user throughput in a cluster of base stations. This is computationally difficult as it requires solving a sparsity-inducing non-convex optimization problem, which is NP-hard. To alleviate the solution complexity, first a stochastic smooth approximation of zero-norm is applied in the antenna power management to enable fast, gradient-based algorithms. For efficient convergence, we develop a novel optimization algorithm combining augmented multiplier (AM) and quadratic programming (QP), and show how this scheme permits decentralized implementation by offloading parts of the computation to the individual base stations to reduce communication overhead. We provide theoretical proof that the proposed algorithm converges both locally and globally under realistic assumptions. Numerical results confirm that our method achieves higher energy efficiency with a superior convergence rate compared to different types of existing methods, and illustrate the relationship between energy efficiency performance and system design parameters.
Author	Cai, Lin X. Zhou, Sheng Niu, Zhisheng Cheng, Yu Yin, Bo Shan, Hangguan Zhang, Shuai
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SubjectTerms	Algorithms Antennas beamforming Computational geometry Computer architecture Convergence Convexity Design parameters distributed optimization Energy conversion efficiency Energy efficiency Massive MIMO Microprocessors MIMO (control systems) Optimization Power control Power management Precoding Quadratic programming Stations Systems design Throughput
Title	Energy-Efficient Massive MIMO With Decentralized Precoder Design
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