Review on Channel Estimation for Reconfigurable Intelligent Surface Assisted Wireless Communication System

Yun Yu, Jinhao Wang, Xiao Zhou (), Chengyou Wang, Zhiquan Bai and Zhun Ye
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Yun Yu: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Jinhao Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Xiao Zhou: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Chengyou Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Zhiquan Bai: School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Zhun Ye: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China

Mathematics, 2023, vol. 11, issue 14, 1-30

Abstract: With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation.

Keywords: wireless communication system; sixth generation (6G); reconfigurable intelligent surface (RIS); channel estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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