Compressive Channel Estimation for IRS-Aided Millimeter-Wave Systems via Two-Stage Lamp Network

In millimeter-wave (mmWave) systems aided by intelligent reflecting surfaces (IRSs), accurate channel estimation under low pilot overhead is challenging because of the large number of passive IRS elements. By exploiting the low-rank nature of mmWave channels in the virtual angular domain (VAD) and the powerful learned approximate message passing (LAMP) network, we propose a two-stage LAMP network with row compression (RCTS-LAMP). Specifically, the two LAMP networks jointly recover the VAD channel by solving two low-dimensional sparse signal recovery problems. Moreover, row compression is adopted between the two networks to further reduce the complexity according to the row sparsity structure. Numerical results show that the estimation performance is increased while the computational complexity can be significantly reduced, which achieves a better trade-off between the accuracy and the complexity.