  Knowledge of the channel state information (CSI) at the transmitter side is
one of the primary sources of information that can be used for the efficient
allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency
Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward
as in TDD systems. Therefore, users usually feed the DL-CSI back to the
transmitter. To remove the need for feedback (and thus having less signaling
overhead), we propose to use two recent deep neural network structures, i.e.,
convolutional neural networks and generative adversarial networks (GANs) to
infer the DL-CSI by observing the UL-CSI. The core idea of our data-driven
scheme is exploiting the fact that both DL and UL channels share the same
propagation environment. As such, we extracted the environment information from
the UL channel response to a latent domain and then transferred the derived
environment information from the latent domain to predict the DL channel. To
overcome incorrect latent domain and the problem of oversimplistic assumptions,
in this work, 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 (BEGAN).
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 in FDD communications.
