RIS: Spatial‑Wideband Effect Analysis and Off‑Grid Channel Estimation

doi:10.12142/ZTECOM.202201008

ZTE Communications ›› 2022, Vol. 20 ›› Issue (1): 57-62.DOI: 10.12142/ZTECOM.202201008

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RIS: Spatial‑Wideband Effect Analysis and Off‑Grid Channel Estimation

JIAN Mengnan^1,²(), ZHANG Nan^1,², CHEN Yijian^1,²

^1.State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518055, China
^2.Algorithm Department, Wireless Product R&D Institute, ZTE Corporation, Shenzhen 518057, China

Received:2022-01-24 Online:2022-03-25 Published:2022-04-06
About author:JIAN Mengnan (jian.mengnan@zte.com.cn) received the B.E. degree in information engineering from Beijing Institute of Technology, China in 2016, and the M.S. degree from Tsinghua University, China in 2019. She is currently an engineer with ZTE Corporation. Her research interests include reconfigurable intelligent surfaces and orbital angular momentum.|ZHANG Nan received the bachelor degree in communication engineering and the master degree in integrated circuit engineering from Tongji University, China in July 2012 and March 2015, respectively. He is now a senior engineer with ZTE Corporation and works on the standardization of LTE and NR systems. His current research interests include channel modeling, MIMO, NOMA techniques, satellite/ATG communications and reconfigurable intelligent surfaces.|CHEN Yijian received the B.S. degree from Central South University, China in 2006. He is currently a senior engineer with ZTE Corporation. His current research interests include massive MIMO, coordinated multi-point transmission, high-frequency communications, and channel modeling.

Abstract

Abstract:

As a critical candidate technology for 5G-advanced and 6G, reconfigurable intelligent surfaces (RIS) have received extensive attention from academia and industry. RIS has the promising features of passiveness, reconfigurable ability, and low cost. RIS channel estimation faces the challenges of high matrix dimension, passive estimation, and spatial-wideband effect. In this article, we analyze the impact of the spatial-wideband effect on the RIS channel to account for the propagation delay across RIS elements and estimate sparse channel parameters such as angle and gain through a super-resolution compressive sensing (CS) algorithm. The simulation results explore the influence of the spatial-wideband effect on the RIS channel and verify the effectiveness of the proposed algorithm.

Key words: RIS, channel estimation, spatial-wideband effect, compressed sensing

JIAN Mengnan, ZHANG Nan, CHEN Yijian. RIS: Spatial‑Wideband Effect Analysis and Off‑Grid Channel Estimation[J]. ZTE Communications, 2022, 20(1): 57-62.

Figures/Tables 3

References 18

1	STRINATI E C, BARBAROSSA S, GONZALEZ-JIMENEZ J L, et al. 6G: the next frontier: from holographic messaging to artificial intelligence using subterahertz and visible light communication [J]. IEEE vehicular technology magazine, 2019, 14(3): 42–50. DOI: 10.1109/MVT.2019.2921162 DOI
2	CUI T J. Information metamaterial and metasurface-from concept to system [C]//43rd International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz). IEEE, 2018: 1–3. DOI: 10.1109/IRMMW-THz.2018.8510031 DOI
3	NGUYEN C L, GEORGIOUS O, GRADONI G, et al. Wireless fingerprinting localization in smart environments using reconfigurable intelligent surfaces [J]. IEEE access, 2021, 9: 135526–135541. DOI: 10.1109/ACCESS.2021.3115596 DOI
4	MISHRA D, JOHANSSON H. Channel estimation and low-complexity beamforming design for passive intelligent surface assisted MISO wireless energy transfer [C]//IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019: 4659–4663. DOI: 10.1109/ICASSP.2019.8683663 DOI
5	HE Z Q, YYAN X. Cascaded channel estimation for large intelligent metasurface assisted massive MIMO [J]. IEEE wireless communications letters, 2019, 9(2): 210–214. DOI: 10.1109/LWC.2019.2948632 DOI
6	JIAN M, ZHAO Y. A modified off-grid SBL channel estimation and transmission strategy for RIS-assisted wireless communication systems [C]//2020 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2020: 1848–1853. DOI: 10.1109/IWCMC48107.2020.9148537 DOI
7	LIU C, LIU X, NG D W K, et al. Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications [J]. IEEE transactions on wireless communications, 2021. DOI: 10.1109/TWC.2021.3100148 DOI
8	TAHA A, ALRABEIAH M, ALKHATEEB A. Enabling large intelligent surfaces with compressive sensing and deep learning [J]. IEEE access, 2021, 9: 44304–44321. DOI: 10.1109/ACCESS.2021.3064073 DOI
9	WANG B, JIAN M, GAO F, et al. Beam squint and channel estimation for wideband mmWave massive MIMO-OFDM systems [J]. IEEE transactions on signal processing, 2019, 67(23): 5893–5908. DOI: 10.1109/TSP.2019.2949502 DOI
10	KIM I, CHOI J. Spatial wideband channel estimation for mmWave massive MIMO systems with hybrid architectures and low-resolution ADCs [J]. IEEE transactions on wireless communications, 2021, 20(6): 4016–4029. DOI: 10.1109/TWC.2021.3054998 DOI
11	WU K, NI W, SU T, et al. Exploiting spatial-wideband effect for fast AoA estimation at lens antenna array [J]. IEEE journal of selected topics in signal processing, 2019, 13(5): 902–917. DOI: 10.1109/JSTSP.2019.2937691 DOI
12	KULKARNI A, MOHSENIN T. Low overhead architectures for OMP compressive sensing reconstruction algorithm [J]. IEEE transactions on circuits and systems I: regular papers, 2017, 64(6): 1468–1480. DOI: 10.1109/TCSI.2017.2648854 DOI
13	JIAN M, GAO F, TIAN Z, et al. Angle-domain aided UL/DL channel estimation for wideband mmWave massive MIMO systems with beam squint [J]. IEEE transactions on wireless communications, 2019, 18(7): 3515–3527. DOI: 10.1109/TWC.2019.2915072 DOI
14	GANESH M, SUBHASHINI K R, JAGAN B. A method of moments based algorithm to synthesize antenna arrays [C]//2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2017: 1091–1095. DOI: 10.1109/WiSPNET.2017.8299931 DOI
15	AZHAR A A, RASHIDN E ABD, MAHMOOD M K A, et al. Effects of mutual coupling and array dissimilarity on angle estimation in MIMO radar [C]//2019 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE). IEEE, 2019: 1–6. DOI: 10.1109/APACE47377.2019.9020903 DOI
16	BJORMSON E, SANGUONETTI L. Power scaling laws and near-field behaviors of massive MIMO and intelligent reflecting surfaces [J]. IEEE open journal of the communications society, 2020, 1: 1306–1324. DOI: 10.1109/OJCOMS.2020.3020925 DOI
17	KAMMOUN A, CHAABAN A, DEBBAH M, et al. Asymptotic max-min SINR analysis of reconfigurable intelligent surface assisted MISO systems [J]. IEEE transactions on wireless communications, 2020, 19(12): 7748–7764. DOI: 10.1109/TWC.2020.2986438 DOI
18	PAN C, REN H, WANG K, et al. Reconfigurable intelligent surfaces for 6G systems: principles, applications, and research directions [J]. IEEE communications magazine, 2021, 59(6): 14–20. DOI: 10.1109/MCOM.001.2001076 DOI

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RIS: Spatial‑Wideband Effect Analysis and Off‑Grid Channel Estimation

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