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辅助毫米波大规模MIMO系统的宽带信道估计问题。由于基站端有源大规模MIMO与无源智能反射面均具有高维度特性，此类系统的信道估计任务极具挑战性。为解决该问题，本文提出一种基于压缩感知的智能反射面辅助毫米波大规模MIMO系统信道估计方案，通过利用毫米波大规模阵列的角域信道稀疏性，在降低导频开销的同时提升信道估计性能。具体而言，我们首先构建了下行导频传输框架。基于视距主导的基站-智能反射面信道已知的先验知识设计导频信号，从而可基于压缩感知理论联合估计基站-用户与智能反射面-用户的高维度信道。此外，为高效实现宽带信道估计，采用分布式正交匹配追踪算法，利用不同子载波信道共有的联合稀疏特性。同时，针对功率泄漏问题设计了冗余字典以增强信道估计性能。仿真结果验证了所提方案的有效性。