Decentralized Beamforming for Cell-Free Massive MIMO With Unsupervised Learning

被引：28

作者：

Hojatian, Hamed ^{[1
]}

Nadal, Jeremy ^{[1
]}

Frigon, Jean-Francois ^{[1
]}

Leduc-Primeau, Francois ^{[1
]}

机构：

[1] Polytech Montreal, Dept Elect Engn, Montreal, PQ H3T 1J4, Canada

来源：

IEEE COMMUNICATIONS LETTERS | 2022年 / 26卷 / 05期

关键词：

Cell-free massive MIMO; hybrid beamforming; deep neural network; decentralized beamforming;

D O I：

10.1109/LCOMM.2022.3157161

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform decentralized coordinated beamforming with zero or limited communication overhead between APs and NC, for both fully digital and hybrid precoding. The proposed DNNs achieve near-optimal sum-rate while also reducing complexity by 10 - 24x compared to conventional near-optimal solutions.

引用

页码：1042 / 1046

页数：5

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