Joint Channel Estimation and Diagnosis for Blocked RIS-Assisted Multi-User Multipath Millimeter-Wave Systems
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摘要: 针对受阻塞无源可重构智能表面(RIS)辅助的多用户毫米波上行链路通信系统，研究了信道估计与阻塞诊断问题。现有研究多聚焦于单用户或单路径场景，该文重点解决多用户多路径共存下的估计难题。通过充分挖掘多用户级联信道的稀疏性与路径间的相关性，提出一种低复杂度的两阶段联合估计与诊断策略。第一阶段选取目标用户，利用高斯-逆伽马先验对阻塞向量的稀疏性进行建模，结合贝叶斯压缩感知技术迭代恢复信道参数与阻塞信息；第二阶段则利用所有用户共享RIS-基站信道且受相同阻塞影响的关键特性，构建公共信道矩阵，以估计其余用户的信道参数。仿真结果表明，所提方法能实现高精度的信道估计与可靠的阻塞诊断。
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关键词:
- 毫米波 /
- 可重构智能超表面(RIS) /
- 多用户多径 /
- 信道估计 /
- 阻塞诊断
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 and8 ).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. -
图 1 所提算法与对比算法在信道估计与阻塞诊断性能上随SNR的变化($ M=64 $, $ Q=32 $, $ {N}_{\text{B}}=8 $, $ L=2 $, $ \{{L}_{u}\}_{u=1}^{U}=2 $)
图 2 信道估计与阻塞诊断性能随目标用户SNR的变化(剩余用户$ \text{SNR}=20\text{ dB} $, $ M=64 $, $ Q=32 $, $ {N}_{\text{B}}=8 $, $ L=2 $, $ \{{L}_{u}\}_{u=1}^{U}=2 $)
图 3 信道估计与阻塞诊断性能随迭代次数$ \text{iter} $的变化($ \text{SNR}=20 $, $ Q=32 $, $ M=64 $, $ {N}_{\text{B}}=8 $, $ L=2 $, $ \{{L}_{u}\}_{u=1}^{U}=2 $)
图 4 信道估计与阻塞诊断性能随时间帧数$ Q $的变化($ \text{SNR}=20 $, $ M=64 $, $ {N}_{\text{B}}=8 $, $ L=2 $, $ \{{L}_{u}\}_{u=1}^{U}=2 $)
图 5 信道估计与阻塞诊断性能随阻塞数$ {N}_{\text{B}} $的变化 ($ \text{SNR}=20 $, $ M=64 $, $ Q=32 $, $ L=2 $, $ \{{L}_{u}\}_{u=1}^{U}=2 $)
图 6 信道估计与阻塞诊断性能随BS天线数$ M $的变化($ \text{SNR}=20 $, $ Q=32 $, $ {N}_{\text{B}}=8 $, $ L=2 $, $ \{{L}_{u}\}_{u=1}^{U}=2 $)
图 7 信道估计与阻塞诊断性能随UE-RIS路径数$ \{{L}_{u}\}_{u=1}^{U} $的变化($ \text{SNR}=20 $，$ M=64 $，$ Q=32 $，$ {N}_{\text{B}}=8 $，$ L=2 $)
图 8 信道估计与阻塞诊断性能随RIS-BS路径数$ L $的变化($ \text{SNR}=20 $，$ M=64 $，$ Q=32 $，$ {N}_{\text{B}}=8 $，$ \{{L}_{u}\}_{u=1}^{U}=2 $)
表 1 第一阶段的算法流程表
算法：目标UE信道估计与阻塞诊断 (1) 输入：选定目标UE发送到BS的信号$ {\boldsymbol{Y}}_{1} $， 设置迭代更新精度$ tol={10}^{-6} $，最大迭代次数$ {T}_{\max }=100 $ (2) 根据式求出$ \boldsymbol{T}({\hat{\mathbf{a}}}) $ (3) 根据求根公式得到$ \{{\hat{\varphi }}_{l}\}_{l=1}^{L} $ (4) 通过式构造$ {{\overline{\boldsymbol{Y}}}}_{1} $ (5) while $ ||{{\tilde{\boldsymbol{Y}}}}_{\text{last}}-{{\tilde{\boldsymbol{Y}}}}_{1}||_{\text{F}}^{2}/||{{\tilde{\boldsymbol{Y}}}}_{1}||_{\text{F}}^{2} \lt tol $或迭代次数达到$ {T}_{\max } $ (6) 更新$ iter=iter+1 $ (7) 更新$ {{\tilde{\boldsymbol{Y}}}}_{\text{last}}={{\tilde{\boldsymbol{Y}}}}_{1} $ (8) 通过式利用OMP求解第$ r $条路经的CSI (9) for $ l=1\colon L(l\neq r) $ (10) 根据式求出$ \Delta {\hat{\theta }}_{l} $与$ \Delta {\hat{\alpha }}_{l} $ (11) end for (12) while$ ||{\boldsymbol{\mu }}_{\text{last}}-\boldsymbol{\mu }||_{2}^{2}/||\boldsymbol{\mu }||_{2}^{2} \lt tol $或迭代次数达到$ {T}_{\max } $ (13) 更新$ {\boldsymbol{\mu }}_{\text{last}}=\boldsymbol{\mu } $ (14) 根据求出阻塞的估计值$ {\hat{\boldsymbol{k}}}=\boldsymbol{\mu } $ (15) 根据(17)更新超参数$ \{\boldsymbol{\alpha },\beta \} $ (16) end while (17) 根据更新$ {\hat{\mathbf{k}}} $，其中$ \delta =1/\text{iter} $ (18) 更新$ {{\tilde{\boldsymbol{Y}}}}_{1}={\boldsymbol{S}}^{\text{T}}\text{diag}(\boldsymbol{b})[{{\hat{\mathbf{h}}}}_{\text{RIS,1}},\ldots ,{{\hat{\boldsymbol{h}}}}_{\text{RIS,}L}] $ (19) end while (20) 通过式利用OMP求解第$ r $条路经的CSI (21) 根据式(13)求出$ \Delta {\hat{\theta }}_{l} $与$ \Delta {\hat{\alpha }}_{l} $ (22) 输出：$ \{{\hat{\varphi }}_{l}\}_{l=1}^{L} $、$ \{\Delta {\hat{\theta }}_{l}\}_{l=1}^{L} $，$ \{\Delta {\hat{\alpha }}_{l}\}_{l=1}^{L} $和$ {\hat{\mathbf{k}}} $ 
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表 2 第二阶段的算法流程表
算法：剩余UE的CSI估计 (1) 输入：$ \{{\hat{\varphi }}_{l}\}_{l=1}^{L} $、$ \{\Delta {\hat{\theta }}_{l}\}_{l=1}^{L} $、$ \{\Delta {\hat{\alpha }}_{l}\}_{l=1}^{L} $、剩余$ U-1 $个UE发送到BS的信号$ {\boldsymbol{Y}}_{u} $。 (2) 根据式(20)和(21)构造$ {{\boldsymbol{\varLambda }}}_{\text{c}} $和$ {\boldsymbol{A}}_{\text{c}} $ (3) 通过$ {{\overline{\boldsymbol{G}}}}_{\text{c}}={\boldsymbol{A}}_{\text{N}}{{\boldsymbol{\varLambda }}}_{\text{c}}\boldsymbol{A}_{\text{c}}^{\text{T}}\text{diag}(\boldsymbol{b}) $构造公共信道$ {{\overline{\boldsymbol{G}}}}_{\text{c}} $ (4) for $ u=2\colon U $ (5) 根据式利用OMP算法求出剩余UE的CSI (6) end for (7) 输出：$ \{{{\hat{\boldsymbol{h}}}}_{\text{c},u}\}_{u=2}^{U} $
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[1] TANG Wankai, CHEN Mingzheng, CHEN Xiangyu, et al. Wireless communications with reconfigurable intelligent surface: Path loss modeling and experimental measurement[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 421–439. doi: 10.1109/TWC.2020.3024887. [2] 郭甜, 张旭辉, 吴雨佳, 等. 可重构智能表面辅助的毫米波信道估计算法[J]. 电子与信息学报, 2023, 45(10): 3612–3621. doi: 10.11999/JEIT221232.GUO Tian, ZHANG Xuhui, WU Yujia, et al. Channel estimation algorithm for reconfigurable intelligent surface aided millimeter wave systems[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3612–3621. doi: 10.11999/JEIT221232. [3] PAN Cunhua, ZHOU Gui, ZHI Kangda, et al. An overview of signal processing techniques for RIS/IRS-aided wireless systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2022, 16(5): 883–917. doi: 10.1109/JSTSP.2022.3195671. [4] 李双志, 杨睿棋, 郭新, 等. 低导频开销RIS辅助毫米波MIMO系统参数化信道估计方案[J]. 通信学报, 2024, 45(9): 82–91. doi: 10.11959/j.issn.1000-436x.2024157.LI Shuangzhi, YANG Ruiqi, GUO Xin, et al. Low pilot overhead parametric channel estimation scheme for RIS-assisted mmWave MIMO systems[J]. Journal on Communications, 2024, 45(9): 82–91. doi: 10.11959/j.issn.1000-436x.2024157. [5] 蒲旭敏, 孙致南, 李静洁, 等. 可重构智能表面中低复杂度毫米波信道估计算法[J]. 电子与信息学报, 2022, 44(7): 2281–2288. doi: 10.11999/JEIT211602.PU Xumin, SUN Zhinan, LI Jingjie, et al. A low complexity millimeter wave channel estimation algorithm in reconfigurable intelligent surface[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2281–2288. doi: 10.11999/JEIT211602. [6] ZHENG Xi, FANG Jun, WANG Hongwei, et al. Compressed CPD-based channel estimation and joint beamforming for RIS-assisted millimeter wave communications[J]. IEEE Transactions on Vehicular Technology, 2024, 73(10): 15214–15226. doi: 10.1109/TVT.2024.3411069. [7] ZHOU Gui, PAN Cunhua, REN Hong, et al. Channel estimation for RIS-aided multiuser millimeter-wave systems[J]. IEEE Transactions on Signal Processing, 2022, 70: 1478–1492. doi: 10.1109/TSP.2022.3158024. [8] PENG Zhendong, PAN Cunhua, ZHOU Gui, et al. Error propagation and overhead reduced channel estimation for RIS-aided multi-user mmWave systems: Invited paper[C]. 2022 International Symposium on Wireless Communication Systems (ISWCS), Hangzhou, China, 2022: 1–6. doi: 10.1109/ISWCS56560.2022.9940350. [9] CHEN Jie, LIANG Yingchang, CHENG H V, et al. Channel estimation for reconfigurable intelligent surface aided multi-user mmWave MIMO systems[J]. IEEE Transactions on Wireless Communications, 2023, 22(10): 6853–6869. doi: 10.1109/TWC.2023.3246264. [10] MEDINA G, JIDA A S, PULIPALI S, et al. Millimeter-wave antenna array diagnosis with partial channel state information[C]. ICC 2021-IEEE International Conference on Communications, Montreal, Canada, 2021: 1–5. doi: 10.1109/ICC42927.2021.9500527. [11] 李双志, 刘聪, 郭新. RIS辅助多用户毫米波系统的联合信道估计和阻塞诊断[J]. 通信学报, 2025, 46(11): 244–253. doi: 10.11959/j.issn.1000-436x.2025222.LI Shuangzhi, LIU Cong, and GUO Xin. Joint channel estimation and blockage diagnosis in RIS-assisted multi-user millimeter-wave systems[J]. Journal on Communications, 2025, 46(11): 244–253. doi: 10.11959/j.issn.1000-436x.2025222. [12] LI Shuangzhi, LEI Haojie, DONG Zheng, et al. Two-stage channel estimation in mmWave MIMO systems with RIS blockage[J]. IEEE Wireless Communications Letters, 2024, 13(12): 3548–3552. doi: 10.1109/LWC.2024.3477504. [13] LI Shuangzhi, LIU Cong, LEI Haojie, et al. Joint channel estimation and blockage diagnosis in RIS-assisted mmWave MIMO-OFDM systems[J]. IEEE Transactions on Vehicular Technology, 2025. doi: 10.1109/TVT.2025.3630168. (查阅网上资料,未找到本条文献卷期页码,请确认). [14] LI Liping, YING Run, LI Yingsong, et al. RIS array diagnosis for mmWave communication systems[J]. IEEE Signal Processing Letters, 2024, 31: 1980–1984. doi: 10.1109/LSP.2024.3427663. [15] BIAN Xuechun, XU Wenbo, WANG Yue, et al. Sparse signal recovery and RIS diagnosis: Double-sparsity based algorithms[C]. GLOBECOM 2023–2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023: 4062–4067. doi: 10.1109/GLOBECOM54140.2023.10437574. [16] GOMES P R B, DE ARAÚJO G T, SOKAL B, et al. Channel estimation in RIS-assisted MIMO systems operating under imperfections[J]. IEEE Transactions on Vehicular Technology, 2023, 72(11): 14200–14213. doi: 10.1109/TVT.2023.3279805. [17] 李双志, 雷豪杰, 郭新. 非理想RIS辅助MIMO系统稀疏信道估计与阵列阻塞诊断[J]. 电子与信息学报, 2025, 47(8): 2573–2583. doi: 10.11999/JEIT241108.LI Shuangzhi, LEI Haojie, and 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. [18] ALKHATEEB A and HEATH R W. Frequency selective hybrid precoding for limited feedback millimeter wave systems[J]. IEEE Transactions on Communications, 2016, 64(5): 1801–1818. doi: 10.1109/TCOMM.2016.2549517. [19] NOH S, SEO K, SUNG Y, et al. Joint direct and indirect channel estimation for RIS-assisted millimeter-wave systems based on array signal processing[J]. IEEE Transactions on Wireless Communications, 2023, 22(11): 8378–8391. doi: 10.1109/TWC.2023.3262492. [20] HE Jiguang, WYMEERSCH H, and JUNTTI M. Channel estimation for RIS-aided mmWave MIMO systems via atomic norm minimization[J]. IEEE Transactions on Wireless Communications, 2021, 20(9): 5786–5797. doi: 10.1109/TWC.2021.3070064. [21] TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666. doi: 10.1109/TIT.2007.909108. [22] ZHOU Zhou, FANG Jun, YANG Linxiao, et al. Low-rank tensor decomposition-aided channel estimation for millimeter wave MIMO-OFDM systems[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(7): 1524–1538. doi: 10.1109/JSAC.2017.2699338. [23] TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1: 211–244. doi: 10.1162/15324430152748236. -
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