Sparsity-Aware Channel Estimation for mmWave Massive MIMO: A Deep CNN-Based Approach

The deep convolutional neural network (CNN) is exploited in this work to conduct the challenging channel estimation for mmWave massive multiple input multiple output (MIMO) systems. The inherent sparse features of the mmWave massive MIMO channels can be extracted and the sparse channel supports can be learnt by the multi-layer CNN-based network through training. Then accurate channel inference can be efficiently implemented using the trained network. The estimation accuracy and spectrum efficiency can be further improved by fully utilizing the spatial correlation among the sparse channel supports of different antennas. It is verified by simulation results that the proposed deep CNN-based scheme significantly outperforms the state-of-the-art benchmarks in both accuracy and spectrum efficiency.