AcsiNet: Attention-Based Deep Learning Network for CSI Prediction in FDD MIMO Systems

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Jiang, Ya;Lin, Wenbin;Zhao, Weikun;Wang, Chaofeng

通讯作者：

Lin, WB

作者机构：

[Jiang, Ya; Lin, Wenbin; Wang, Chaofeng; Zhao, Weikun; Lin, WB] Univ South China, Sch Comp Sci, Hengyang 421001, Peoples R China.

[Lin, Wenbin; Lin, WB] Univ South China, Sch Math & Phys, Hengyang 421001, Peoples R China.

通讯机构：

[Lin, WB ] U

Univ South China, Sch Comp Sci, Hengyang 421001, Peoples R China.

Univ South China, Sch Math & Phys, Hengyang 421001, Peoples R China.

语种：

英文

关键词：

Downlink;Uplink;Deep learning;Transmission line matrix methods;Three-dimensional displays;Feature extraction;Training;CSI prediction;deep learning;attention mechanism;FDD massive MIMO;planar antenna array

期刊：

IEEE WIRELESS COMMUNICATIONS LETTERS

ISSN：

2162-2337

年：

2023

卷：

期：

页码：

471-475

DOI：

10.1109/LWC.2022.3230950

基金类别：

National Natural Science Foundation of China [62201248]

机构署名：

本校为第一且通讯机构

院系归属：

数理学院

摘要：

In 5G frequency division duplex (FDD) systems, the user equipment needs to feedback the measured downlink channel state information (CSI) to the base station to improve the throughput. For massive multiple-input-multiple-output (MIMO) systems, each antenna in base station needs its CSI feedback, which results in significant transmission overhead and latency. We propose an attention-based deep learning network to directly predict the downlink CSI from the corresponding uplink one, eliminating the feedback overhead completely. Specifically, the uplink CSI is first compressed based on the 3D inve...

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AcsiNet: Attention-Based Deep Learning Network for CSI Prediction in FDD MIMO Systems

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