A Unified Deep Learning Method for CSI Feedback in Massive MIMO Systems

被引：0

作者：

GAO Zhengguang ^{[1
]}

LI Lun ^{[1
]}

WU Hao ^{[1
]}

TU Xuezhen ^{[2
]}

HAN Bingtao ^{[1
]}

机构：

[1] State Key Laboratory of Mobile Network and Mobile Multimedia Technology

[2] The College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics

来源：

ZTE Communications | 2022年 / 20卷 / 04期

关键词：

deep learning; channel state information; element filling strategy;

D O I：

暂无

中图分类号：

TN929.5 [移动通信]; TP18 [人工智能理论];

学科分类号：

080402 ; 080904 ; 0810 ; 081001 ; 081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A unified deep learning(DL) based algorithm is proposed for channel state information(CSI) compression in massive multipleinput multiple-output(MIMO) systems. More importantly, the element filling strategy is investigated to address the problem of model redesign-ing and retraining for different antenna typologies in practical systems. The results show that the proposed DL-based algorithm achieves better performance than the enhanced Type Ⅱ algorithm in Release 16 of 3GPP. The proposed element filling strategy enables one-time training of a unified model to compress and reconstruct different channel state matrices in a practical MIMO system.

引用

页码：110 / 115

页数：6

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