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This letter considers the transceiver design in frequency division duplex(FDD) massive multiple-input multiple-output (MIMO) orthogonal frequencydivision multiplexing (OFDM) systems for high-quality data transmission. Wepropose a novel deep learning based framework where the procedures of pilotdesign, channel feedback, and hybrid beamforming are realized by carefullycrafted deep neural networks. All the considered modules are jointly learned inan end-to-end manner, and a graph neural network is adopted to effectivelycapture interactions between beamformers based on the built graphicalrepresentation. Numerical results validate the effectiveness of our method.

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems