Towards a Quantitative Analysis of CSI for AI/ML Based Sensing

Channel State Information (CSI) sensing is now an established element of Integrated Sensing and Communication (ISAC) operations, but what is its real potential, and what are its limits? The literature focused more on sophisticated AI systems to exploit CSI variations imposed by different propagation scenarios, indeed achieving amazing results, but few, if any works tackled the topic of characterizing the long-term CSI behavior, its stability, and its stochastic properties to achieve insight in the potential and limits of CSI sensing. This work presents a first attempt in this direction, providing a framework that allows the comparison of CSIs quantifying the difference between CSI collected in different scenarios and showing that a quantitative analysis of the CSI is possible, and it can also help to explain the accuracy difference observed between distinct experiments with a CNN-based localization method taken from the literature.