  Knowledge of channel state information (CSI) is fundamental to many
functionalities within the mobile wireless communications systems. With the
advance of machine learning (ML) and digital maps, i.e., digital twins, we have
a big opportunity to learn the propagation environment and design novel methods
to derive and report CSI. In this work, we propose to combine untrained neural
networks (UNNs) and conditional generative adversarial networks (cGANs) for
MIMO channel recreation based on prior knowledge. The UNNs learn the prior-CSI
for some locations which are used to build the input to a cGAN. Based on the
prior-CSIs, their locations and the location of the desired channel, the cGAN
is trained to output the channel expected at the desired location. This
combined approach can be used for low overhead CSI reporting as, after
training, we only need to report the desired location. Our results show that
our method is successful in modelling the wireless channel and robust to
location quantization errors in line of sight conditions.
