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

• Published in: Mathematics (Basel) Vol. 11; no. 14; p. 3235
• Main Authors: Yu, Yun, Wang, Jinhao, Zhou, Xiao, Wang, Chengyou, Bai, Zhiquan, Ye, Zhun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2023
• Subjects: Algorithms; Analysis; Antennas; channel estimation; Communications systems; Deep learning; Electromagnetism; Energy consumption; Frequency analysis; Internet of Things; Low frequencies; Maintenance costs; Mathematics; Metamaterials; Power consumption; Quality of service; reconfigurable intelligent surface (RIS); Reconfigurable intelligent surfaces; Signal processing; sixth generation (6G); Spectrum allocation; System reliability; Transmission efficiency; wireless communication system; Wireless communication systems; Wireless communications
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11143235

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.