Deep UL2DL: Data-Driven Channel Knowledge Transfer From Uplink to Downlink

Deep UL2DL: Data-Driven Channel Knowledge Transfer From Uplink to Downlink

To remove the need for signaling overhead of feedback channels for transmitter channel state information (CSI) in Frequency Division Duplexing (FDD), we propose using convolutional neural networks and generative adversarial networks (GANs) to infer the downlink (DL) CSI by observing the uplink (UL)...

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Bibliographic Details
Main Authors: Mohammad Sadegh Safari, Vahid Pourahmadi, Shabnam Sodagari
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Vehicular Technology
Subjects:
Channel Prediction
Convolutional Neural Networks
Deep Learning
Downlink
FDD Systems
Generative Adversarial Networks
Online Access:https://ieeexplore.ieee.org/document/8944056/

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