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