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

• 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/

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) CSI. Our data-driven scheme exploits the fact that both DL and UL channels share the same propagation environment. As such, we extracted the environment information from UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel. To prevent incorrect latent domain and the problem of oversimplistic assumptions, we did not use any specific parametric model and, instead, used data-driven approaches to discover the underlying structure of data without any prior model assumptions. To overcome the challenge of capturing the UL-DL joint distribution, we used a mean square error-based variant of the GAN structure with improved convergence properties called boundary equilibrium GAN. For training and testing we used simulated data of Extended Vehicular-A (EVA) and Extended Typical Urban (ETU) models. Simulation results verified that our methods can accurately infer and predict the downlink CSI from the uplink CSI for different multipath environments.