Wideband mmWave MIMO: One Prior-Aided LAMP-Based Network Combining with Residual Learning for Beamspace Channel Estimation

Abstract

Existing channel estimation schemes for wideband systems generally estimate the channel matrix by pre-estimating the common support, which has a limited validity owing to the effect of beam squint. It is worth noting that the learned approximate message passing (LAMP) network need not pre-estimate the support and has obtained a relatively reliable estimation quality in the narrowband systems. In order to degrade the performance penalty caused by inaccurate estimation of the support in the wideband millimeter-wave MIMO systems, a prior-aided Gaussian mixture LAMP (GM-LAMP) network combining with residual learning is presented. Specifically, the multicarrier Gaussian mixture threshold shrinkage function is constructed for the GM-LAMP network, which can directly estimate the wideband beamspace channel while avoiding pre-estimating the support; then, considering the impact of channel noise and the coarse estimation error by the GM-LAMP, a residual network (ResNet) is designed to improve the estimation performance. Simulation results validate the efficiency of the proposed network (referred to as GLAMP-ResNet) with the lower computational complexity compared with the existing schemes.