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arXiv · 2602.24150

Channel Estimation for Beyond Diagonal RIS Exploiting Core Tensor Sparsity

Daniel Costa Araújo·André L. F. de Almeida
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Abstract

Beyond diagonal reconfigurable intelligent surfaces (BD-RISs) 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 the 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 to enhance robustness to noise. 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.

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Daniel Costa Araújo, André L. F. de Almeida. 2026-02-27. Channel Estimation for Beyond Diagonal RIS Exploiting Core Tensor Sparsity. https://doi.org/10.1109/lwc.2026.3708040

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