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...
| Main Authors: | , , , , |
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| 其他作者: | |
| 格式: | Journal Article |
| 語言: | English |
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2023
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/172075 |
| _version_ | 1826127684227301376 |
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| author | Yang, Songjie Lyu, Wanting Xiu, Yue Zhang, Zhongpei Yuen, Chau |
| 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 |
| author_sort | Yang, Songjie |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T07:12:33Z |
| format | Journal Article |
| id | ntu-10356/172075 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:12:33Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-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. |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Electrical and electronic engineering Active Double-Reconfigurable Intelligent Surface Multi-User Two-timescale Channel Estimation |
| url | https://hdl.handle.net/10356/172075 |
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