Joint Sparsity and Low-Rank Minimization for Reconfigurable Intelligent Surface-Assisted Channel Estimation
Joint Sparsity and Low-Rank Minimization for Reconfigurable Intelligent Surface-Assisted Channel Estimation
复制标题
DOI：
10.1109/tcomm.2023.3331521
复制
发表时间：
2024-03
影响因子：
8.3
通讯作者：
Jie Tang;Xiaoyu Du;Zhen Chen;Xiuyin Zhang;D. So;Kai-Kit Wong;Jonathon A. Chambers
中科院分区：
文献类型：
--
作者：
Jie Tang;Xiaoyu Du;Zhen Chen;Xiuyin Zhang;D. So;Kai-Kit Wong;Jonathon A. Chambers
可重构智能表面（RISs）由于能够配置无线传播环境而在毫米波系统中受到广泛关注。然而，由于发射机和接收机之间存在RIS，需要估计大量的信道系数，导致更多的导频开销。在本文中，我们提出了一种联合稀疏和低秩的两阶段信道估计方案的RIS辅助毫米波系统。具体而言，我们首先建立一个低秩近似模型对噪声信道，适合的前提条件下，压缩感知理论的完美信号恢复。为了克服低秩问题求解的困难，提出了一种迹算子来代替传统的核范数算子，它能更好地逼近矩阵的秩。此外，利用毫米波信道的稀疏特性，在第二阶段进行稀疏恢复以估计RI辅助信道。仿真结果表明，该方案实现了显着的性能增益的估计精度相比，基准计划。
Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect signal recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate the RIS-assisted channel in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.