Sparse Channel Estimation and Array Blockage Diagnosis for Non-Ideal RIS-Assisted MIMO Systems
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摘要: 针对非理想可重构智能超表面(RIS)辅助毫米波多输入多输出(MIMO)系统信道状态信息获取问题，该文提出一种稀疏级联信道参数与阵列阻塞向量联合估计方案。首先，设计信道训练帧结构，将接收信号建模为张量模型。然后，基于张量的平行因子分解模型，分析毫米波信道参数与阻塞向量之间的内在关联，实现对收发端空域信道参数的有效估计。基于这些空间角频率，构建出同时反映剩余信道参数和阻塞信息的耦合观测矩阵。最后，通过利用多径信道和阻塞向量的双稀疏特性，完成剩余信道参数的估计和阻塞诊断。仿真结果表明，所提方案的信道估计和阻塞诊断性能表现优于对照方案。
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关键词:
- 毫米波MIMO /
- 可重构智能超表面(RIS) /
- 信道估计 /
- 阻塞诊断
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. -
表 1 本文数学符号对照表
数学符号 符号说明 ${{\boldsymbol{A}}^*}$ 共轭 ${{\boldsymbol{A}}^\dagger }$ 伪逆 $\diamondsuit $ Khatri-Rao积 $ \otimes $ Kronecker积 ${[{\boldsymbol{a}}]_n}$ 向量${\boldsymbol{a}}$的第$n$个元素 ${[\mathcal{A}]_{(n)}}$ 张量$\mathcal{A}$的模式$n$展开 ${\text{diag(}} \cdot {\text{)}}$ 对角化 ${\text{vec(}} \cdot {\text{)}}$ 向量化 ${\text{unve}}{{\text{c}}_{M \times N}}( \cdot )$ 反向量化 $\left| \cdot \right|$ 取模 $ {\left\| \cdot \right\|_{\mathrm{F}}} $ Frobenius范数 $ {\left\| \cdot \right\|_{\mathrm{p}}} $ ${\mathrm{p}}$-范数 $\mathbb{E}\{ \cdot \} $ 均值 
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1 基于双稀疏的RIS辅助毫米波MIMO联合信道估计与阻塞诊断
(1) 输入：接收信号${{\boldsymbol{y}}_{k,t}}$，预编码信号${{\boldsymbol{p}}_t}$，组合矩阵${\boldsymbol{W}}$，相移向量${{\boldsymbol{s}}_k}$，$t = 1,2, \cdots ,T$，$k = 1,2, \cdots ,K$ (2) 根据导频传输协议，构造接收信号的张量模型$\mathcal{Y} \in {\mathbb{C}^{{N_{\text{B}}} \times T \times K}}$ (3) 构造张量$\mathcal{Y}$的模式1和模式2展开形式${[\mathcal{Y}]_{(1)}}$和${[\mathcal{Y}]_{(2)}}$，见式(14) (4) 构造1维字典矩阵$ {{{\bar {\boldsymbol A}}}_{\text{B}}} \in {\mathbb{C}^{{M_{\text{B}}} \times {{\bar L}_{\text{B}}}}} $和$ {{{\bar {\boldsymbol A}}}_{\text{U}}} \in {\mathbb{C}^{{M_{\text{U}}} \times {{\bar L}_{\text{U}}}}} $ (5) 根据式(15)和式(16)，使用OMP算法估计${{\boldsymbol{\hat \theta }}_{\text{B}}}$和${{\boldsymbol{\hat \theta }}_{\text{U}}}$ (6) 根据${{\boldsymbol{\hat \theta }}_{\text{B}}}$和${{\boldsymbol{\hat \theta }}_{\text{U}}}$构$ {{{\hat {\boldsymbol A}}}_{\text{B}}} = [{\boldsymbol{a}}({\hat \theta _{{\text{B,}}1}}),{\boldsymbol{a}}({\hat \theta _{{\text{B,}}2}}), \cdots ,{\boldsymbol{a}}({\hat \theta _{{\text{B,}}{L_{\text{B}}}}})] $和${{{\hat {\boldsymbol A}}}_{\text{U}}} = [{\boldsymbol{a}}({\hat \theta _{{\text{U,}}1}}),{\boldsymbol{a}}({\hat \theta _{{\text{U,}}2}}), \cdots ,{\boldsymbol{a}}({\hat \theta _{{\text{U,}}{L_{\text{U}}}}})]$ (7) 构造张量$\mathcal{Y}$的模式3展开形式${[\mathcal{Y}]_{(3)}}$，见式(14) (8) 根据${[\mathcal{Y}]_{(3)}}$计算$ {{\bar {\boldsymbol B}}} = {[\mathcal{Y}]_{(3)}}{({({{\boldsymbol{P}}^{\text{T}}}{{\hat {\boldsymbol A}}}_{\text{U}}^*{{\boldsymbol{\varOmega }}_{\text{U}}}\diamondsuit {{\boldsymbol{W}}^{\text{T}}}{{{\hat {\boldsymbol A}}}_{\text{B}}}{{\boldsymbol{\varOmega }}_{\text{B}}})^{\text{T}}})^\dagger } $ (9) 根据式(12)将剩余信道参数和阻塞向量的观测矩阵表示为$ \begin{array}{*{20}{c}} {{{\bar {\boldsymbol B}}} = {{\boldsymbol{S}}^{\text{T}}}({\boldsymbol{\bar H}} + {\boldsymbol{D}}) + {\boldsymbol{\bar N}}} \end{array} $ (10) 初始化阻塞向量${{\boldsymbol{e}}^{(0)}} = {{{{\textit{1}}}}_N}$，即信道偏移矩阵${{\boldsymbol{D}}^{(0)}} = {{{{\textit{0}}}}_N}$ (11) $i \leftarrow i + 1$ (12) for $l = 1:L$ (13) 根据式(25)，使用截断SVD分解求解$ {\boldsymbol{\hat b}}_{{\text{v}},l}^{(i)} $和$ {\boldsymbol{\hat b}}_{h,l}^{(i)} $ (14) 根据式(26)估计$ \hat {\boldsymbol{b}}_{{\text{v}},l}^{(i)} $和$ \hat {\boldsymbol{b}}_{{\text{h}},l}^{(i)} $，并重新构造$ {\boldsymbol{\hat b}}_{{\text{v}},l}^{(i)} $和$ {\boldsymbol{\hat b}}_{h,l}^{(i)} $ (15) 根据式(27)计算$ \hat \alpha _l^{(i)} $，并更新$ {{\boldsymbol{\hat{ \bar {\boldsymbol{H}}}}}^{(i)}} = \hat \alpha _l^{(i)}({\boldsymbol{\hat b}}_{{\text{v}},l}^{(i)} \otimes {\boldsymbol{\hat b}}_{{\text{h}},l}^{(i)}) $ (16) end (17) 根据式(28)，确定$ {{\bar {\boldsymbol B}}}_2^{(i)} $中强度最大的列的索引${l_{{\text{LoS}}}}$，使用ADMM算法求解式(30)中的稀疏问题 (18) 判断${\left\| {{{\boldsymbol{e}}^{(i)}} - {{\boldsymbol{e}}^{(i - 1)}}} \right\|_2} \le \tau $或者$i \le {I_1}$是否满足，不满足则返回步骤(11) (19) 输出：${{\boldsymbol{\hat \theta }}_{\text{U}}}$, ${{\boldsymbol{\hat \theta }}_{\text{B}}}$, ${{\boldsymbol{\hat b}}_{\text{v}}}$, ${{\boldsymbol{\hat b}}_{\text{h}}}$, ${\boldsymbol{\hat \alpha }}$和${\boldsymbol{\hat e}}$
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