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)

Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-16

Available Online: 2026-04-15

Abstract

Abstract

Objective Although Reconfigurable intelligent surface (RIS) can effectively modulate Millimeter-Wave (mmWave) signals to reshape wireless environments, its elements are susceptible to weather and physical obstructions in practice, causing unpredictable distortions that necessitate joint channel estimation and blockage diagnosis. While most existing work focuses on single-user systems, multi-user scenarios remain underexplored—presenting a key opportunity to leverage the commonality of RIS blockages and RIS-Base station (BS) paths across users. This paper proposes a low-complexity framework exploiting the sparsity and correlation of multi-user cascaded channels for joint estimation and diagnosis. Methods Based on the premise that all User Equipments (UEs) share the same RIS-BS channel and a common RIS blockage, we decompose the problem into two stages. First, a target UE is selected, where we exploit the dual sparsity of the mmWave channel and blockage vector, along with linear dependencies among RIS-BS paths, to formulate a sparse recovery problem. This is solved via a hierarchical Bayesian model using an efficient sparse Bayesian learning algorithm for joint recovery. Second, partial Channel State Information (CSI) from the target UE constructs a common coupling matrix that integrates the RIS-BS channel and blockage, reformulating channel estimation for the remaining UEs as another sparse recovery problem. Results and Discussions This paper proposes a low-complexity strategy for cascaded channel estimation and blockage diagnosis by exploiting the sparsity and correlation of multi-user cascaded channels and leveraging RIS blockage commonality. Ideal estimation results serve as a theoretical lower bound, against which the proposed algorithm and two benchmark schemes are compared. Simulation results demonstrate that the proposed algorithm consistently outperforms the benchmarks (Fig. 1). Key findings include: higher target user SNR improves NMSE, highlighting selection importance (Fig. 2); strong convergence with increasing iterations (Fig. 3); closer approximation to the ideal case as time frames increase (Fig. 4); robustness under increased blockage (Fig. 5); performance gains from more base station antennas leveraging array orthogonality (Fig. 6); superior estimation with slightly lower runtime via path correlations (Table 3); and accuracy reduction with increasing path count due to higher model complexity (Figs. 7 and 8). Conclusions This paper proposes a joint channel estimation and blockage diagnosis framework for blocked RIS-assisted multi-user mmWave systems. Simulations show the method closely approaches the theoretical performance bound in complex multipath environments. It maintains performance advantages under high blockage rates while reducing pilot overhead and computational complexity via common channel structures. The work mitigates performance degradation in practical RIS deployments, clarifies key parameter impacts, and offers insights for system design. As practical blockages often exhibit block or structured sparsity, a promising direction is to incorporate structured priors (e.g., group sparsity, Markov random fields) into the SBL framework to capture spatial correlations and enhance diagnostic accuracy and robustness.
- Millimetre-Wave,
- Reconfigurable intelligent surface (RIS),
- Multiuser multipath,
- Channel estimation,
- Blockage diagnosis

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References(23)

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