Deep Learning-enhanced Massive Channel Estimation for Reconfigurable Intelligent Surface-aided Massive Machine-Type Communication

被引：0
作者：
Liu, Ting [1 ]
Wang, Yuan [2 ]
Xin, Yuanxue [3 ]
机构：
[1] School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing
[2] School of Computer Science, Nanjing University of Information Science and Technology, Nanjing
[3] College of Information Science and Engineering, Hohai University, Changzhou
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Channel estimation; Deep Learning (DL); Grant-free access; Massive Machine-Type Communication (mMTC); Reconfigurable Intelligent Surface (RIS);
D O I：
10.11999/JEIT240584
中图分类号：
学科分类号：
摘要：
Massive Machine-Type Communication (mMTC) is one of the typical scenarios of the fifth-generation mobile communications systems, and nearly one million devices per square kilometer can be connected under this circumstance. The Reconfigurable Intelligent Surface (RIS) is applied for the grant-free uplink transmission due to the complexity of the propagation environment in the scenario of massive connectivity. Then, the cascaded channel, i.e., the channel link between devices and the RIS, as well as the channel link between the RIS and the Base Station (BS), is formed. Consequently, the quality of the wireless signal transmission can be controlled effectively. On this basis, a denoising learning system is designed using the principle of turbo decoding message passing. The RIS-aided cascaded CSI is learned and estimated through a large number of training data. In addition, the statistical analysis of the RIS-assisted mMTC channel estimation is performed to verify the accuracy of the proposed scheme. Numerical simulation results and theoretical analyses show that the proposed technique is superior to other compressed-sensing-type methods. © 2024 Science Press. All rights reserved.
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页码：4002 / 4008
页数：6
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