Geometric Mean Decomposition Based Hybrid Precoding for Millimeter-Wave Massive MIMO

被引:40
|
作者
Xie, Tian [1 ]
Dai, Linglong [1 ]
Gao, Xinyu [1 ]
Shakir, Muhammad Zeeshan [2 ]
Li, Jianjun [3 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China
[2] Univ West Scotland, Sch Engn & Comp, Glasgow, Lanark, Scotland
[3] Zhongyuan Univ Technol, Sch Elect & Informat Engn, Zhengzhou 450007, Henan, Peoples R China
基金
中国国家自然科学基金;
关键词
Millimeter-wave Massive MIMO; hybrid precoding; geometric mean decomposition; bit allocation; ANALOG; SYSTEMS;
D O I
10.1109/CC.2018.8388000
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Hybrid precoding can reduce the number of required radio frequency (RF) chains in millimeter-Wave (mmWave) massive MIMO systems. However, existing hybrid precoding based on singular value decomposition (SVD) requires the complicated bit allocation to match the different signal-to-noise-ratios (SNRs) of different sub-channels. In this paper, we propose a geometric mean decomposition (GMD)-based hybrid precoding to avoid the complicated bit allocation. Specifically, we seek a pair of analog and digital precoders sufficiently close to the unconstrained fully digital GMD precoder. To achieve this, we fix the analog precoder to design the digital precoder, and vice versa. The analog precoder is designed based on the orthogonal matching pursuit (OMP) algorithm, while GMD is used to obtain the digital precoder. Simulations show that the proposed GMD-based hybrid precoding achieves better performance than the conventional SVD-based hybrid precoding with only a slight increase in complexity.
引用
收藏
页码:229 / 238
页数:10
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