Channel Estimation for Beyond Diagonal RIS Exploiting Core Tensor Sparsity
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918360223055872 |
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| author | Araújo, Daniel Costa de Almeida, André L. F. |
| author_facet | Araújo, Daniel Costa de Almeida, André L. F. |
| contents | Beyond diagonal reconfigurable intelligent surface (BD-RIS)s enhance wave manipulation through inter-element couplings but pose significant channel estimation challenges due to cascaded channels and block-Kronecker structures. This paper proposes a compressive sensing framework exploiting sparse Tucker decomposition of the measurement tensor and the Kronecker rank-one structure of channel components. Two algorithms are developed: Sparse Tensor Orthogonal Recovery Method (STORM), which uses orthogonal matching pursuit (OMP) for greedy support recovery, and Sparse Tensor subspace- Aided Recovery (STAR), which leverages subspace-based projection for enhanced noise robustness. Both perform joint sparse support identification, followed by a Kronecker rank-one factorization via singular value decomposition (SVD) to recover the channel parameters. Simulations show that STAR achieves oracle-assisted least squares (LS) performance at moderate-to-high signal-to-noise ratio (SNR) with significantly fewer measurements than baseline methods, enabling practical BD-RIS deployment in next-generation millimeter wave (mmWave)/sub-terahertz (sub-THz) networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_24150 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Channel Estimation for Beyond Diagonal RIS Exploiting Core Tensor Sparsity Araújo, Daniel Costa de Almeida, André L. F. Signal Processing Beyond diagonal reconfigurable intelligent surface (BD-RIS)s enhance wave manipulation through inter-element couplings but pose significant channel estimation challenges due to cascaded channels and block-Kronecker structures. This paper proposes a compressive sensing framework exploiting sparse Tucker decomposition of the measurement tensor and the Kronecker rank-one structure of channel components. Two algorithms are developed: Sparse Tensor Orthogonal Recovery Method (STORM), which uses orthogonal matching pursuit (OMP) for greedy support recovery, and Sparse Tensor subspace- Aided Recovery (STAR), which leverages subspace-based projection for enhanced noise robustness. Both perform joint sparse support identification, followed by a Kronecker rank-one factorization via singular value decomposition (SVD) to recover the channel parameters. Simulations show that STAR achieves oracle-assisted least squares (LS) performance at moderate-to-high signal-to-noise ratio (SNR) with significantly fewer measurements than baseline methods, enabling practical BD-RIS deployment in next-generation millimeter wave (mmWave)/sub-terahertz (sub-THz) networks. |
| title | Channel Estimation for Beyond Diagonal RIS Exploiting Core Tensor Sparsity |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.24150 |