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For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO)systems, hybrid processing architecture is essential to significantly reducethe complexity and cost but is quite challenging to be jointly optimized overthe transmitter and receiver. In this paper, deep learning (DL) is applied todesign a novel joint hybrid processing framework (JHPF) that allows end-to-endoptimization by using back propagation. The proposed framework includes threeparts: hybrid processing designer, signal flow simulator, and signaldemodulator, which outputs the hybrid processing matrices for the transceiverby using neural networks (NNs), simulates the signal transmission over the air,and maps the detected symbols to the original bits by using the NN,respectively. By minimizing the cross-entropy loss between the recovered andoriginal bits, the proposed framework optimizes the analog and digitalprocessing matrices at the transceiver jointly and implicitly instead ofapproximating pre-designed label matrices, and its trainability is provedtheoretically. It can be also directly applied to orthogonal frequency divisionmultiplexing systems by simply modifying the structure of the training data.Simulation results show the proposed DL-JHPF outperforms the existing hybridprocessing schemes and is robust to the mismatched channel state informationand channel scenarios with the significantly reduced runtime.

Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems