Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems
Junyi Yang, Weifeng Zhu, Shu Sun, Xiaofeng Li, Xingqin Lin, Meixia Tao
Abstract
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.
Topics & Concepts
Computer scienceOrthogonal frequency-division multiplexingBeamformingMIMOMIMO-OFDMMultiplexingChannel (broadcasting)TransceiverArtificial neural networkElectronic engineeringDuplex (building)Artificial intelligenceTelecommunicationsWirelessEngineeringBiologyDNAGeneticsAntenna Design and OptimizationAdvanced MIMO Systems OptimizationAntenna Design and Analysis