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Hybrid beamforming (HB) has emerged as a promising technology to supportultra high transmission capacity and with low complexity for Millimeter Wave(mmWave) multiple-input and multiple-output (MIMO) system. However, the designof digital and analog beamformer is a challenge task with non-convexoptimization, especially for the multi-user scenario. Recently, the blooming ofdeep learning research provides a new vision for the signal processing ofcommunication system. In this work, we propose a deep neural network based HBfor the multi-User mmWave massive MIMO system, referred as DNHB. The HB systemis formulated as an autoencoder neural network, which is trained in a style ofend-to-end self-supervised learning. With the strong representation capabilityof deep neural network, the proposed DNHB exhibits superior performance thanthe traditional linear processing methods. According to the simulation results,DNHB outperforms about 2 dB in terms of bit error rate (BER) performancecompared with existing methods.

Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach