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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3040619