  Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave
(mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach
for improved coverage and throughput. Most existing work assumes the ideal
channel estimation, which can be challenging due to the high-dimensional
cascaded MIMO channels and passive reflecting elements. Therefore, this paper
proposes a deep denoising neural network assisted compressive channel
estimation for mmWave IRS systems to reduce the training overhead.
Specifically, we first introduce a hybrid passive/active IRS architecture,
where very few receive chains are employed to estimate the uplink user-to-IRS
channels. At the channel training stage, only a small proportion of elements
will be successively activated to sound the partial channels. Moreover, the
complete channel matrix can be reconstructed from the limited measurements
based on compressive sensing, whereby the common sparsity of angular domain
mmWave MIMO channels among different subcarriers is leveraged for improved
accuracy. Besides, a complex-valued denoising convolution neural network
(CV-DnCNN) is further proposed for enhanced performance. Simulation results
demonstrate the superiority of the proposed solution over state-of-the-art
solutions.
