计算机科学
残余物
频道(广播)
架空(工程)
一般化
方案(数学)
极高频率
算法
信号(编程语言)
电子工程
计算机工程
实时计算
人工智能
电信
数学
工程类
数学分析
操作系统
程序设计语言
出处
期刊:IEEE Wireless Communications Letters
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:12 (7): 1179-1183
被引量:1
标识
DOI:10.1109/lwc.2023.3265648
摘要
Reconfigurable intelligent surface (RIS) is promising for enhancing millimeter wave signal coverage. However, traditional channel estimation (CE) methods have high complexity and pilot overhead due to RIS’s passive nature and a large number of unit cells. Recently, deep learning (DL) has shown the potential in improving communication system performance. This letter proposes a DL-based scheme for estimating the cascaded channel in a RIS-assisted communication system. The proposed scheme utilizes the global attention residual network, which considers multi-channel information fusion on the channel feature matrices to improve CE matrix accuracy. Simulation results demonstrate that the proposed scheme significantly improves CE accuracy and has good generalization performance.
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