Partially-Connected Hybrid Beamforming for Multi-User Massive MIMO Systems

被引:5
|
作者
Zang, Guangda [1 ]
Hu, Lingna [2 ]
Yang, Feng [1 ]
Ding, Lianghui [3 ]
Liu, Hui [1 ]
机构
[1] Shanghai Jiao Tong Univ, Inst Wireless Commun, Shanghai 200240, Peoples R China
[2] Shanghai Inst Satellite Engn, Shanghai 200240, Peoples R China
[3] Shanghai Jiao Tong Univ, Inst Image Commun & Network Engn, Shanghai 200240, Peoples R China
来源
IEEE ACCESS | 2020年 / 8卷
关键词
Massive MIMO; hybrid beamforming; SINR constraints; penalty method; penalty dual decomposition; DESIGN; ANALOG; TRANSCEIVERS; OPTIMIZATION;
D O I
10.1109/ACCESS.2020.3040508
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the high power consumption and hardware cost of radio frequency (RF) chains, the conventional fully-digital beamforming will be impractical for large-scale antenna systems (LSAS). To address this issue, hybrid beamforming has been proposed to reduce the number of RF chains. However, the fully-connected structure assumed in most hybrid beamforming schemes is still cost-intensive. Recently, the partially-connected structure employing notably fewer phase shifters has received considerable attention in both academia and industry. But the design of partially-connected hybrid beamforming has not been fully understood, especially in multi-user systems. In this article, we directly address the challenging non-convex non-smooth partially-connected hybrid beamforming design problem with individual signal-to-interference-plus-noise ratio (SINR) constraints and unit-modulus constraints in a multi-user massive multiple-input multiple-output (MIMO) system. An iterative alternating algorithm based on a penalty method is proposed to obtain a stationary point, which inevitably has relatively high computational complexity. Thus, two low-complexity algorithms are then proposed by utilizing matrix approximation. Numerical results demonstrate significant performance gains of the proposed algorithms over existing hybrid beamforming algorithms. Moreover, the proposed low-complexity algorithms can achieve near-optimal performance with dramatically reduced computational complexity.
引用
收藏
页码:215287 / 215298
页数:12
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