DNN Based Multi-Path Beamforming for FDD Millimeter-Wave Massive MIMO Systems

In this paper, we propose a deep neural network (DNN) based beamforming scheme for frequency-division-duplex (FDD) millimeter-Wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Different from the time-division-duplex (TDD) systems, for FDD systems the channel reciprocity between the down-link (DL) and up-link (UL) channels does not hold in general, requiring an extra channel state information (CSI) feedback stage. Based on the previous theoretical analysis and measurements, however, partial reciprocities, including the spatial directional angles and number of propagation paths, do exist for FDD mmWave systems. With this partial reciprocity, we propose a multi-path beamforming scheme with a predefined codebook. Different from most previous works that only focus on one dominant path of each mobile station (MS), this work considers a multi-path scenario where the proposed scheme identifies all the propagation paths of all MSs, and selects the optimal combination of codewords with the help of a DNN that not only overcomes angle ambiguity but also significantly reduces computational complexity.