Machine Learning for CSI Recreation Based on Prior Knowledge

TL;DR

This paper introduces a novel machine learning approach combining UNNs and cGANs to accurately recreate MIMO channel state information using prior knowledge and digital maps, reducing reporting overhead.

Contribution

It proposes a new method that leverages untrained neural networks and conditional GANs for efficient CSI recreation based on prior knowledge and location data.

Findings

Effective channel modeling and recreation in line of sight conditions.

Robustness to location quantization errors.

Reduced overhead in CSI reporting.

Abstract

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.