Model-Driven Deep Learning-Based Sparse Channel Representation and Recovery for Wideband mmWave Massive MIMO Systems

Jianqiao Chen, Fanyang Meng, Nan Ma, Xiaodong Xu, Ping Zhang

Abstract: In this paper, we exploit a novel model-driven deep learning (MDDL)-based scheme for efficient wideband millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) channel estimation where the neural networks for sparse channel representation and recovery are respectively designed. For the former, we propose an angular-resampling network to determine the sampling intervals of grids adaptive to the true angles of arrival (AoAs) of paths, based on which an effective dictionary for sparse channel representation in angle-domain can be constructed. To this end, the neural network consisting of three modules trained with a newly-designed angular Gaussian-mixture distribution loss function is developed. For the latter, we propose an inverse-free variational Bayesian learning (IF-VBL) driven deep-unfolding network for sparse channel recovery. Specifically, the IF-VBL method by maximizing a general relaxed evidence lower bound (ELBO) is first developed, which is then unfolded into a layer-wise architecture where some a-priori parameters are learned. Simulation results verify the superiority of the proposed MDDL-based channel estimation scheme with significantly improved convergence and performance over counterparts.