Multi-task deep learning based hybrid precoding for mmWave massive MIMO system

Due to the different signal-to-noise ratio (SNR) of each subchannel, the bit error rate (BER) of hybrid precoding based on singular value decomposition (SVD) decreases. In this paper, we propose a multi-task learning based precoding network (PN) model to solve the BER loss problem caused by SVD based hybrid precoding under imperfect channel state information (CSI). Specifically, we firstly generate a dataset including imcomplete CSI input channel matrix and corresponding output labels to train the PN model. The output labels are designed based on uniform channel decomposition (UCD) which decomposes the channel into multiple subchannels with same gain, while the vertical-bell layered space-time structure (V-BLAST) signal processing technology is combined to eliminate the inner interference of the subchannels. Then, the PN model is trained to design the analog and digital precoding/combining matrix simultaneous. Simulation results show that the proposed scheme has only negligible gap in spectrum efficiency compared with the fully digital precoding, while achieves better BER performance than SVD based hybrid precoding.