Sparse Channel Estimation and Array Blockage Diagnosis for Non-Ideal RIS-Assisted MIMO Systems

LI Shuangzhi; LEI Haojie; GUO Xin

doi:10.11999/JEIT241108

Volume 47 Issue 8

Aug. 2025

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LI Shuangzhi, LEI Haojie, GUO Xin. Sparse Channel Estimation and Array Blockage Diagnosis for Non-Ideal RIS-Assisted MIMO Systems[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2573-2583. doi: 10.11999/JEIT241108

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LI Shuangzhi, LEI Haojie, GUO Xin. Sparse Channel Estimation and Array Blockage Diagnosis for Non-Ideal RIS-Assisted MIMO Systems[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2573-2583. doi: 10.11999/JEIT241108

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Sparse Channel Estimation and Array Blockage Diagnosis for Non-Ideal RIS-Assisted MIMO Systems

doi: 10.11999/JEIT241108 cstr: 32379.14.JEIT241108

School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (61901416), The Young Elite Scientists Sponsorship Program of Henan (2024HYTP026), The Natural Science Foundation of Henan (242300420269), The Science and Technology Development Project of Henan Province (242102211017)

Received Date: 2024-11-08
Rev Recd Date: 2025-06-29

Available Online: 2025-07-08

Publish Date: 2025-08-27

Abstract

Abstract

Objective Reconfigurable Intelligent Surfaces (RISs) offer a promising approach to enhance Millimeter-Wave (mmWave) Multiple-Input Multiple-Output (MIMO) systems by dynamically manipulating wireless propagation. However, practical deployments are challenged by hardware faults and environmental blockages (e.g., dust or rain), which impair Channel State Information (CSI) accuracy and reduce Spectral Efficiency (SE). Most existing studies either overlook the interdependence between the CSI and blockage vector or fail to leverage the dual sparsity of multipath channels and blockage patterns. This study proposes a joint sparse channel estimation and blockage diagnosis scheme to overcome these limitations, thereby enabling reliable beamforming and enhancing system robustness in non-ideal RIS-assisted mmWave MIMO environments. Methods A third-order Parallel Factor (PARAFAC) decomposition model is constructed for the received signals using a tensor-based signal representation. The intrinsic relationship between mmWave channel parameters and the blockage vector is exploited to estimate spatial angular frequencies at the User Equipment (UE) and Base Station (BS) using Orthogonal Matching Pursuit (OMP). Based on these frequencies, a coupled observation matrix is formed to jointly capture residual channel parameters and blockage vector information. This matrix is reformulated as a Least Absolute Shrinkage and Selection Operator (LASSO) problem, which is solved using the Alternating Direction Method of Multipliers (ADMM) to estimate the blockage vector. The remaining channel parameters are then recovered using sparse reconstruction techniques by leveraging their inherent sparsity. Iterative refinement updates both the blockage vector and channel parameters, ensuring convergence under limited pilot overhead conditions. Results and Discussions For a non-ideal RIS-assisted mmWave MIMO system (Fig. 1), a signal transmission framework is designed (Fig. 2), in which the received signals are represented as a third-order tensor. Leveraging the dual-sparsity of multipath channels and the blockage vector, a joint estimation scheme is developed (Algorithm 1), enabling effective parameter decoupling through tensor-based parallel factor decomposition and iterative optimization. Simulation results show that the proposed scheme achieves superior performance in both channel estimation and blockage diagnosis compared with baseline methods by fully exploiting dual-sparsity characteristics (Fig. 3). SE analysis confirms the detrimental effect of blockages on system throughput and highlights that the proposed scheme improves SE by compensating for blockage-induced impairments (Fig. 4). The method also demonstrates strong estimation accuracy under reduced pilot overhead (Fig. 5) and improved robustness as the number of blocked RIS elements increases (Fig. 6). A decline in spatial angular frequency estimation is observed with fewer UE antennas, which negatively affects overall performance; however, estimation stabilizes as antenna count increases (Fig. 7). Moreover, when Non-Line-of-Sight (NLoS) path contributions decrease, the scheme exhibits enhanced performance due to improved resolution between Line-of-Sight (LoS) and NLoS components (Fig. 8). Conclusions This study proposes a joint channel estimation and blockage diagnosis scheme for non-ideal RIS-assisted mmWave MIMO systems, based on the dual sparsity of multipath channels and blockage vectors. Analysis of the tensor-based parallel factor decomposition model reveals that the estimation of spatial angular frequencies at the UE and BS is unaffected by blockage conditions. The proposed scheme accounts for the contributions of NLoS paths, enabling accurate decoupling of residual channel parameters and blockage vector across different propagation paths. Simulation results confirm that incorporating NLoS path information improves both channel estimation accuracy and blockage detection. Compared with existing methods, the proposed approach achieves superior performance in both aspects. In practical scenarios, real-time adaptability may be challenged if blockage states vary more rapidly than channel characteristics. Future work will focus on enhancing the scheme’s responsiveness to dynamic blockage conditions.
- Millimeter-Wave MIMO,
- Reconfigurable Intelligent Surface (RIS),
- Channel estimation,
- Blockage diagnosis

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References(28)

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