Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem o...

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Main Authors: Yang, Songjie, Lyu, Wanting, Xiu, Yue, Zhang, Zhongpei, Yuen, Chau
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/172075
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1720752023-11-21T05:40:27Z Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning Yang, Songjie Lyu, Wanting Xiu, Yue Zhang, Zhongpei Yuen, Chau School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Active Double-Reconfigurable Intelligent Surface Multi-User Two-timescale Channel Estimation Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels' sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead. Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework. Ministry of Education (MOE) This work was supported in part by the Natural Science Foundation of Shenzhen City under Grant JCYJ20210324140002008; in part by the Natural Science Foundation of Sichuan Province under Grant 2022NSFSC0489; and in part by the Ministry of Education, Singapore, through its MOE Tier 2 under Award MOE-T2EP50220-0019. 2023-11-21T05:40:27Z 2023-11-21T05:40:27Z 2023 Journal Article Yang, S., Lyu, W., Xiu, Y., Zhang, Z. & Yuen, C. (2023). Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning. IEEE Transactions On Communications, 71(6), 3605-3620. https://dx.doi.org/10.1109/TCOMM.2023.3265115 0090-6778 https://hdl.handle.net/10356/172075 10.1109/TCOMM.2023.3265115 2-s2.0-85153353093 6 71 3605 3620 en MOE-T2EP50220-0019 IEEE Transactions on Communications © 2023 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Active Double-Reconfigurable Intelligent Surface
Multi-User Two-timescale Channel Estimation
spellingShingle Engineering::Electrical and electronic engineering
Active Double-Reconfigurable Intelligent Surface
Multi-User Two-timescale Channel Estimation
Yang, Songjie
Lyu, Wanting
Xiu, Yue
Zhang, Zhongpei
Yuen, Chau
Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning
description Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels' sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead. Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Yang, Songjie
Lyu, Wanting
Xiu, Yue
Zhang, Zhongpei
Yuen, Chau
format Article
author Yang, Songjie
Lyu, Wanting
Xiu, Yue
Zhang, Zhongpei
Yuen, Chau
author_sort Yang, Songjie
title Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning
title_short Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning
title_full Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning
title_fullStr Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning
title_full_unstemmed Active 3D double-RIS-aided multi-user communications: two-timescale-based separate channel estimation via Bayesian learning
title_sort active 3d double-ris-aided multi-user communications: two-timescale-based separate channel estimation via bayesian learning
publishDate 2023
url https://hdl.handle.net/10356/172075
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