Channel Estimation for IRS Aided MIMO System with Neural Network Solution

被引：0
|
作者：
Gu, ZhiJian [1 ]
He, Chunlong [1 ]
Huang, Zanhai [1 ]
Xiao, Ming [1 ]
机构：
[1] Shenzhen Univ, Shenzhen, Peoples R China
来源：
2023 IEEE 98TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-FALL | 2023年
关键词：
Intelligent Reflective Surface (IRS); Residual Neural Network; Multiple-Input Multiple-Output (MIMO); Deep learning (DL);
D O I：
10.1109/VTC2023-Fall60731.2023.10333685
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Intelligent Reflective Surface (IRS) is a promising technique for Beyond 5G and 6G wireless communications. IRS is comprised of plenty of passive reflecting elements that an external controller is able to control by software. To develop the great potential of IRS-aided wireless communication system, we should get a full understanding of Channel State Information (CSI). However, it's an exceedingly challengeable task for channel estimation in IRS-aided system. In this paper, we propose a residual neural network to achieve cascaded channel estimation for an IRS-aided multiple-input multiple-output (MIMO) communication system. Numerical results prove the effectiveness and high robustness of our Deep Learning (DL) method.
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页数：5
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