This paper investigates the broadband channel estimation (CE) for intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) massive MIMO systems. The CE for such systems is a challenging task due to the large dimension of both the active massive MIMO at the base station (BS) and passive IRS. To address this problem, this paper proposes a compressive sensing (CS)-based CE solution for IRS-aided mmWave massive MIMO systems, whereby the angular channel sparsity of large-scale array at mmWave is exploited for improved CE with reduced pilot overhead. Specifically, we first propose a downlink pilot transmission framework. By designing the pilot signals based on the prior knowledge that the line-of-sight dominated BS-to-IRS channel is known, the high-dimensional channels for BS-to-user and IRS-to-user can be jointly estimated based on CS theory. Moreover, to efficiently estimate broadband channels, a distributed orthogonal matching pursuit algorithm is exploited, where the common sparsity shared by the channels at different subcarriers is utilized. Additionally, the redundant dictionary to combat the power leakage is also designed for the enhanced CE performance. Simulation results demonstrate the effectiveness of the proposed scheme.

翻译：本文研究了面向智能反射面（IRS）辅助的毫米波（mmWave）大规模MIMO系统的宽带信道估计（CE）问题。由于基站（BS）端有源大规模MIMO与无源IRS均具有大维度特性，此类系统的信道估计极具挑战性。为解决该难题，本文提出了一种基于压缩感知（CS）的IRS辅助毫米波大规模MIMO系统信道估计方案，利用毫米波大规模阵列的角域信道稀疏性，以降低导频开销并提升估计性能。具体而言，我们首先设计了一种下行导频传输框架。基于视线主导的基站-IRS信道已知这一先验知识进行导频信号设计，使得基站-用户与IRS-用户的联合高维信道可通过CS理论进行估计。此外，为高效估计宽带信道，采用分布式正交匹配追踪算法，利用不同子载波信道的公共稀疏性。同时，为抑制功率泄露设计了冗余字典，进一步增强了信道估计性能。仿真结果验证了所提方案的有效性。