Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning
To solve the problem of high feedback overhead in a multi-user massive multiple-input multiple-output (MIMO) system assisted by a reconfigurable intelligent surface (RIS) in frequency-division duplexing (FDD) mode, a channel state information (CSI) feedback framework based on manifold learning was p...
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China InfoCom Media Group
2024-12-01
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Series: | 物联网学报 |
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Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00374/ |
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author | QIAN Mujun YU Shunchi LIU Chen SONG Yunchao LU Feng |
author_facet | QIAN Mujun YU Shunchi LIU Chen SONG Yunchao LU Feng |
author_sort | QIAN Mujun |
collection | DOAJ |
description | To solve the problem of high feedback overhead in a multi-user massive multiple-input multiple-output (MIMO) system assisted by a reconfigurable intelligent surface (RIS) in frequency-division duplexing (FDD) mode, a channel state information (CSI) feedback framework based on manifold learning was proposed. Firstly, the framework achieved initial feedback overhead reduction by simplifying the CSI feedback process. Then, the framework combined the manifold learning to train two set of dictionaries to achieve dimension reduction and reconstruction of incremental CSI. Finally, the original channel was restored at the base station. The simulation results show that the CSI feedback scheme proposed in this paper has lower overhead and complexity than the existing methods in the multi-user and limited scattering environment, and the reconstruction quality is significantly improved. |
format | Article |
id | doaj-art-04e50ef9783d4770ab7afc8465e3b43e |
institution | Kabale University |
issn | 2096-3750 |
language | zho |
publishDate | 2024-12-01 |
publisher | China InfoCom Media Group |
record_format | Article |
series | 物联网学报 |
spelling | doaj-art-04e50ef9783d4770ab7afc8465e3b43e2025-01-25T19:00:26ZzhoChina InfoCom Media Group物联网学报2096-37502024-12-01816717679606170Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learningQIAN MujunYU ShunchiLIU ChenSONG YunchaoLU FengTo solve the problem of high feedback overhead in a multi-user massive multiple-input multiple-output (MIMO) system assisted by a reconfigurable intelligent surface (RIS) in frequency-division duplexing (FDD) mode, a channel state information (CSI) feedback framework based on manifold learning was proposed. Firstly, the framework achieved initial feedback overhead reduction by simplifying the CSI feedback process. Then, the framework combined the manifold learning to train two set of dictionaries to achieve dimension reduction and reconstruction of incremental CSI. Finally, the original channel was restored at the base station. The simulation results show that the CSI feedback scheme proposed in this paper has lower overhead and complexity than the existing methods in the multi-user and limited scattering environment, and the reconstruction quality is significantly improved.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00374/massive MIMOFDDRISchannel feedbackmanifold learning |
spellingShingle | QIAN Mujun YU Shunchi LIU Chen SONG Yunchao LU Feng Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning 物联网学报 massive MIMO FDD RIS channel feedback manifold learning |
title | Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning |
title_full | Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning |
title_fullStr | Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning |
title_full_unstemmed | Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning |
title_short | Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning |
title_sort | research on csi feedback of ris assisted massive mimo system based on manifold learning |
topic | massive MIMO FDD RIS channel feedback manifold learning |
url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2024.00374/ |
work_keys_str_mv | AT qianmujun researchoncsifeedbackofrisassistedmassivemimosystembasedonmanifoldlearning AT yushunchi researchoncsifeedbackofrisassistedmassivemimosystembasedonmanifoldlearning AT liuchen researchoncsifeedbackofrisassistedmassivemimosystembasedonmanifoldlearning AT songyunchao researchoncsifeedbackofrisassistedmassivemimosystembasedonmanifoldlearning AT lufeng researchoncsifeedbackofrisassistedmassivemimosystembasedonmanifoldlearning |