Semi-Passive RIS-Aided sequential channel estimation and prediction
Haider, Mirza A. Zhang, Yimin D. Ding, Yanwu Shen, Dan Pham, Khanh D. Chen, Genshe
Haider, Mirza A.
Zhang, Yimin D.
Ding, Yanwu
Shen, Dan
Pham, Khanh D.
Chen, Genshe
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2024-08-26
Type
Conference paper
Genre
Keywords
Channel estimation,Neural network,Reconfigurable intelligent surface,Sequential learning,Sparse array
Subjects (LCSH)
Citation
M. A. Haider, Y. D. Zhang, Y. Ding, D. Shen, K. Pham and G. Chen, "Semi-Passive RIS-Aided Sequential Channel Estimation and Prediction," 2024 IEEE 13rd Sensor Array and Multichannel Signal Processing Workshop (SAM), Corvallis, OR, USA, 2024
Abstract
The popularity of mmWave in 5G and future communications is hindered by challenging propagation environments, such as line-of-sight obstruction. Reconfigurable intelligent surfaces (RIS) address this issue by dynamically modifying wireless channels, thereby enhancing data rates, reducing latency, and improving reliability in non-line-of-sight scenarios. For high data-rate communication and precise mobile user localization, a large RIS is required, resulting in a high pilot overhead for channel estimation. To address this issue, we exploit a semi-passive RIS with sparsely distributed active RIS elements in lieu of fully passive RIS. This approach efficiently enables channel estimation both between the base station and the RIS as well as between the RIS and the mobile users. Structured covariance matrix interpolation optimally utilizes the array aperture from the sparsely placed active RIS elements. Recognizing the need for frequent channel estimation, we introduce a recurrent neural network-based model for sequential channel prediction, resulting in a significant reduction of the required training pilot signals. Simulation results affirm the capability and effectiveness of the proposed approach to enhance data transmission. © 2024 IEEE.
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Publisher
IEEE Computer Society
Journal
Book Title
Series
13rd IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2024
8 July 2024 through 11 July 2024
202104
8 July 2024 through 11 July 2024
202104
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
ISSN
2151-870X