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.

翻译：本文针对频分双工（FDD）大规模多输入多输出（MIMO）正交频分复用（OFDM）系统中的收发机设计问题，旨在实现高质量数据传输。我们提出了一种新颖的基于深度学习的框架，其中导频设计、信道反馈和混合波束赋形等流程由精心构建的深度神经网络实现。所有相关模块以端到端方式联合学习，并采用图神经网络基于建立的图表示方法有效捕获波束赋形器之间的交互作用。数值结果验证了所提方法的有效性。