Joint Channel Estimation and Diagnosis for Blocked RIS-Assisted Multi-User Multipath Millimeter-Wave Systems

LI Shuangzhi; LIU Cong; WANG Ning; HAN Gangtao; GUO Xin

doi:10.11999/JEIT260093

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LI Shuangzhi, LIU Cong, WANG Ning, HAN Gangtao, GUO Xin. Joint Channel Estimation and Diagnosis for Blocked RIS-Assisted Multi-User Multipath Millimeter-Wave Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260093

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LI Shuangzhi, LIU Cong, WANG Ning, HAN Gangtao, GUO Xin. Joint Channel Estimation and Diagnosis for Blocked RIS-Assisted Multi-User Multipath Millimeter-Wave Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260093

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Joint Channel Estimation and Diagnosis for Blocked RIS-Assisted Multi-User Multipath Millimeter-Wave Systems

doi: 10.11999/JEIT260093 cstr: 32379.14.JEIT260093

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

Funds: The National Natural Science Foundation of China (61901416), The Natural Science Foundation of Henan Province (242300420269, 252300421887)

Received Date: 2026-01-24
Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-16

Available Online: 2026-04-15

Abstract

Abstract

Objective Reconfigurable Intelligent Surface (RIS) can effectively modulate Millimeter-Wave (mmWave) signals and reshape the wireless propagation environment. In practical deployments, however, RIS elements are vulnerable to adverse weather and physical obstructions, which cause unpredictable distortion and motivate joint channel estimation and blockage diagnosis. Most existing studies focus on single-user systems, whereas multi-user scenarios remain insufficiently studied. This gap creates an opportunity to exploit the common RIS blockage vector and the shared RIS-Base Station (BS) channel across users. This paper therefore proposes a low-complexity framework for joint channel estimation and blockage diagnosis by exploiting the sparsity and correlation of multi-user cascaded channels. Methods Under the assumption that all User Equipment (UE) shares the same RIS-BS channel and is affected by a common RIS blockage vector, the problem is divided into two stages. First, a target UE is selected. The sparsity of the mmWave channel and blockage vector, together with the linear dependence among RIS-BS paths, is used to formulate a sparse recovery problem. A hierarchical Bayesian model is then adopted, and an efficient Sparse Bayesian Learning (SBL) algorithm is used for joint recovery. Second, partial Channel State Information (CSI) obtained from the target UE is used to construct a common channel matrix that combines the RIS-BS channel and blockage information. Channel estimation for the remaining UEs is then reformulated as another sparse recovery problem. Results and Discussions A low-complexity strategy for cascaded channel estimation and blockage diagnosis is developed by exploiting the sparsity and correlation of multi-user cascaded channels and the commonality of the RIS blockage vector. Ideal estimation results are used as a theoretical lower bound, and the proposed algorithm is compared with two benchmark schemes. Simulation results show that the proposed algorithm consistently outperforms the benchmark schemes (Fig. 1). Specifically, a higher target-user Signal-to-Noise Ratio (SNR) improves the Normalized Mean Square Error (NMSE), which confirms the importance of target-user selection (Fig. 2). The algorithm also shows good convergence as the number of iterations increases (Fig. 3), and its performance approaches the ideal case more closely as the number of time frames increases (Fig. 4). In addition, the method remains robust as the number of blocked elements increases (Fig. 5). More BS antennas further improve performance by enhancing array orthogonality (Fig. 6). By exploiting path correlation, the proposed method achieves better estimation accuracy with slightly lower runtime (Table 1). However, estimation accuracy decreases as the number of paths increases because the model becomes more complex (Figs. 7 and 8). Conclusions This paper proposes a joint channel estimation and blockage diagnosis framework for blocked RIS-assisted multi-user multipath mmWave systems. Simulation results show that the method approaches the theoretical performance bound in complex multipath environments. It also maintains clear performance advantages under high blockage rates while reducing computational complexity through the use of common channel structures. This study provides a practical solution to performance degradation in RIS deployment, clarifies the effects of key parameters, and offers guidance for system design. Because practical blockages often exhibit block-sparse or structured-sparse characteristics, future work may incorporate structured priors, such as group sparsity and Markov random fields, into the SBL framework to capture spatial correlation and improve diagnostic accuracy and robustness.
- Millimetre-Wave,
- Reconfigurable Intelligent Surface (RIS),
- Multiuser multipath,
- Channel estimation,
- Blockage diagnosis

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References(23)

References

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