  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.
