RIS-Aided Near-Field Channel Estimation under Mutual Coupling and Spatial Correlation
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arXiv
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| Format: | Preprint |
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2025
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| author | Dkhan, Ahmad Tarboush, Simon Sarieddeen, Hadi Al-Naffouri, Tareq Y. |
| author_facet | Dkhan, Ahmad Tarboush, Simon Sarieddeen, Hadi Al-Naffouri, Tareq Y. |
| contents | The integration of reconfigurable intelligent surfaces (RIS) with extremely large multiple-input multiple-output (MIMO) arrays at the base station has emerged as a key enabler for enhancing wireless network performance. However, this setup introduces high-dimensional channel matrices, leading to increased computational complexity and pilot overhead in channel estimation. Mutual coupling (MC) effects among densely packed unit cells, spatial correlation, and near-field propagation conditions further complicate the estimation process. Conventional estimators, such as linear minimum mean square error (MMSE), require channel statistics that are challenging to acquire for high-dimensional arrays, while least squares (LS) estimators suffer from performance limitations. To address these challenges, the reduced-subspace least squares (RS-LS) estimator leverages array geometry to enhance estimation accuracy. This work advances the promising RS-LS estimation algorithm by explicitly incorporating MC effects into the more realistic and challenging near-field propagation environment within the increasingly relevant generalized RIS-aided MIMO framework. Additionally, we investigate the impact of MC on the spatial degrees of freedom (DoF). Our analysis reveals that accounting for MC effects provides a significant performance gain of approximately 5 dB at an SNR of 5 dB, compared to conventional methods that ignore MC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01828 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RIS-Aided Near-Field Channel Estimation under Mutual Coupling and Spatial Correlation Dkhan, Ahmad Tarboush, Simon Sarieddeen, Hadi Al-Naffouri, Tareq Y. Signal Processing The integration of reconfigurable intelligent surfaces (RIS) with extremely large multiple-input multiple-output (MIMO) arrays at the base station has emerged as a key enabler for enhancing wireless network performance. However, this setup introduces high-dimensional channel matrices, leading to increased computational complexity and pilot overhead in channel estimation. Mutual coupling (MC) effects among densely packed unit cells, spatial correlation, and near-field propagation conditions further complicate the estimation process. Conventional estimators, such as linear minimum mean square error (MMSE), require channel statistics that are challenging to acquire for high-dimensional arrays, while least squares (LS) estimators suffer from performance limitations. To address these challenges, the reduced-subspace least squares (RS-LS) estimator leverages array geometry to enhance estimation accuracy. This work advances the promising RS-LS estimation algorithm by explicitly incorporating MC effects into the more realistic and challenging near-field propagation environment within the increasingly relevant generalized RIS-aided MIMO framework. Additionally, we investigate the impact of MC on the spatial degrees of freedom (DoF). Our analysis reveals that accounting for MC effects provides a significant performance gain of approximately 5 dB at an SNR of 5 dB, compared to conventional methods that ignore MC. |
| title | RIS-Aided Near-Field Channel Estimation under Mutual Coupling and Spatial Correlation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.01828 |