Near-Field Channel Estimation for Extremely-Large Single-RF-Chain RIS Systems
Schroeder, Rafaela; He, Jiguang; Juntti, Markku (2024-10-07)
IEEE
07.10.2024
R. Schroeder, J. He and M. Juntti, "Near-Field Channel Estimation for Extremely-Large Single-RF-Chain RIS Systems," 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Lucca, Italy, 2024, pp. 266-270, doi: 10.1109/SPAWC60668.2024.10694366
https://rightsstatements.org/vocab/InC/1.0/
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412037025
https://urn.fi/URN:NBN:fi:oulu-202412037025
Tiivistelmä
Abstract
This paper studies near-field (NF) channel estimation (CE) for extremely-large hybrid reconfigurable intelligent surface (RIS)-aided systems. To be specific, we consider uplink training, where the mobile station sends pilot signals to the base station and the hybrid RIS, which is equipped with a single radio frequency chain for sensing purposes. We use the linear total variation regularization (TVR) algorithm to leverage the inherent structured sparsity of NF channels within the angular domain. We further evaluate the impact of carrier frequency, power-splitting factor, and hybrid RIS array configurations on the normalized mean square error (NMSE) performance of the TVR algorithm. Numerical results indicate that the TVR algorithm exhibits superior NMSE performance particularly at higher frequencies, such as 28 GHz. In addition, we observe that the power-splitting factor plays a pivotal role in the NMSE performance.
This paper studies near-field (NF) channel estimation (CE) for extremely-large hybrid reconfigurable intelligent surface (RIS)-aided systems. To be specific, we consider uplink training, where the mobile station sends pilot signals to the base station and the hybrid RIS, which is equipped with a single radio frequency chain for sensing purposes. We use the linear total variation regularization (TVR) algorithm to leverage the inherent structured sparsity of NF channels within the angular domain. We further evaluate the impact of carrier frequency, power-splitting factor, and hybrid RIS array configurations on the normalized mean square error (NMSE) performance of the TVR algorithm. Numerical results indicate that the TVR algorithm exhibits superior NMSE performance particularly at higher frequencies, such as 28 GHz. In addition, we observe that the power-splitting factor plays a pivotal role in the NMSE performance.
Kokoelmat
- Avoin saatavuus [38840]