Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Knowledge of channel state information (CSI) is fundamental to many functionalities for mobile communication 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-CSI estimates, 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 CSI recreation method is successful in modelling the wireless channel under different configurations of prior-CSI spatial sampling. In addition, the results consider a real world measurement campaign for indoor line of sight and non-line of sight channels. The signal to noise ratio (SNR) achieved by our CSI recreation is better than the SNR reported by the measured campaign providers. Moreover, our CSI recreation provides means for low overhead CSI reporting as the UNN structure is underparameterized compared to the full explicit CSI, and only the desired location is needed for the cGAN to recreate the desired CSI.