Intelligent Distributed Beam Selection for Cell-Free Massive MIMO Hybrid Precoding

被引：0

作者：

Zhang, Cheng ^{[1
,2
]}

Zhang, Xiaojin ^{[1
,2
]}

Wei, Lan ^{[1
,2
]}

Xu, Qinzhen ^{[1
,2
]}

Huang, Yongming ^{[1
,2
]}

Zhang, Wei ^{[3
,4
]}

机构：

[1] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China

[2] Purple Mt Labs, Nanjing 211111, Peoples R China

[3] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia

[4] Purple Mt Labs, Nanjing 211111, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2023年 / 27卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Cell-free; hybrid precoding; unsupervised learning; potential game; distributed algorithm;

D O I：

10.1109/LCOMM.2023.3317312

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Hybrid precoding in cell-free massive MIMO (CF-mMIMO) faces many challenges, e.g., large cascaded beamspace and high signaling overhead. In this letter, we propose a two-stage distributed beam selection for CF- mMIMO hybrid precoding. Each base station (BS) first learns an efficient beamspace compression with a deep neural network (DNN) from the local received signal strength indicator (RSSI), based on which good beam-selection labels are obtained with a proposed potential game algorithm. By learning the mapping from local RSSI to the corresponding label, a DenseNet-based online local beam selection can be realized at each BS. Simulations verify the effectiveness of our proposed scheme.

引用

页码：2973 / 2977

页数：5

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