Deep Learning-Based CSI Feedback for Terahertz Ultra-Massive MIMO Systems

被引：0
|
作者：
Li, Yuling [1 ]
Guo, Aihuang [1 ]
机构：
[1] Tongji Univ, Dept Informat & Commun Engn, Shanghai 201804, Peoples R China
来源：
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | 2024年 / E107A卷 / 08期
关键词：
CSI feedback; UM-MIMO system; terahertz communication; deep learning; channel characteristic;
D O I：
10.23919/transfun.2023EAL2089
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Terahertz (THz) ultra-massive multiple-input multiple- output (UM-MIMO) is envisioned as a key enabling technology of 6G wireless communication. In UM-MIMO systems, downlink channel state information (CSI) has to be fed to the base station for beamforming. However, the feedback overhead becomes unacceptable because of the large antenna array. In this letter, the characteristic of CSI is explored from the perspective of data distribution. Based on this characteristic, a novel network named Attention-GRU Net (AGNet) is proposed for CSI feedback. Simulation results show that the proposed AGNet outperforms other advanced methods in the quality of CSI feedback in UM-MIMO systems.
引用
收藏
页码：1413 / 1416
页数：4
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