Deep-Learning-Based Phase-Only Robust Massive MU-MIMO Hybrid Beamforming

被引：6

作者：

Almagboul, Mohammed A. ^{[1
]}

Shu, Feng ^{[1
,2
,3
]}

Abdelgader, Abdeldime M. S. ^{[4
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Nanjing 210094, Peoples R China

[2] Fujian Agr & Forestry Univ, Coll Comp & Informat Sci, Fuzhou 350002, Peoples R China

[3] Fuzhou Univ, Coll Phys & Informat, Fuzhou 350116, Peoples R China

[4] Karary Univ, Coll Engn, Elect & Comp Dept, Omdurman 12304, Sudan

来源：

IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Radio frequency; Loading; Covariance matrices; Estimation; Uplink; Direction-of-arrival estimation; Array signal processing; Hybrid beamforming; deep-learning; robust adaptive beamforming (RAB); MIMO; DOA ESTIMATION; CHANNEL ESTIMATION; LOW-COMPLEXITY; SYSTEMS;

D O I：

10.1109/LCOMM.2021.3070077

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Conventional hybrid beamforming (BF) techniques encounter high computational complexity (CC) and performance loss due to array steering vector mismatches. Therefore, in this letter, a joint robust adaptive BF (RAB) method based on the diagonal loading technique along with phase-only digital beamformer design is proposed. In addition, with the aim of reducing the CC of the system, a novel deep-learning model is proposed to estimate the digital weights. Simulations demonstrated that the proposed deep neural network (DNN) model can have similar performance for digital BF weights estimation as a metaheuristic-based one with significantly lower CC.

引用

页码：2280 / 2284

页数：5

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