Deep Unfolding-Based Channel Estimation for IRS-Aided mmWave Systems via Two-Stage LAMP Network With Row Compression

Intelligent reflecting surface (IRS) is emerging as a promising and revolutionary technology for achieving cost-effective wireless communication systems. However, the performance of IRS communications heavily relies on acquiring accurate channel state information (CSI), which is challenging under low training overhead due to a large number of passive IRS elements. This paper proposes a two-stage LAMP network with row compression (RCTS-LAMP) to solve the joint estimation problem of direct and cascaded channels in IRS-aided millimeter-wave (mmWave) systems. Specifically, the proposed RCTS-LAMP is a model-driven neural network that combines the advantages of compressive sensing (CS) and deep learning (DL) by using the deep unfolding technique. By doing so, we can recover the direct and cascaded channels with CS under low training overhead, and the estimation performance can be significantly improved with the joint optimization of DL. Meanwhile, the cascaded channel estimation is decomposed into two stages to reduce computational complexity further. Numerical results show that the RCTS-LAMP network can estimate the cascaded channel with a better trade-off between computational complexity and accuracy, while the direct channel can be jointly recovered without adding an extra network.