Deep Learning-Based Downlink Channel Estimation for FDD Massive MIMO Systems

被引：3
|
作者：
Xiang, Bingtong [1 ]
Hu, Die [1 ]
Wu, Jun [1 ]
机构：
[1] Fudan Univ, Sch Comp Sci, Shanghai 200438, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2023年 / 12卷 / 04期
基金：
中国国家自然科学基金;
关键词：
FDD; massive MIMO; downlink channel estimation; deep learning; NETWORKS;
D O I：
10.1109/LWC.2023.3240512
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This letter is concerned with the downlink channel estimation in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) system. With the number of antennas increased, acquiring the downlink channel state information (CSI) becomes complex, thus restricts the performance of communication systems. A deep learning based algorithm is used to estimate the downlink CSI without the feedback. In the proposed method, the uplink cluster is firstly obtained from the receiving signals. Based on the uplink cluster data, the downlink channel is then estimated by a neural network. Simulation results show that the proposed algorithm can achieve higher achievable spectral efficiency.
引用
收藏
页码：699 / 702
页数：4
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