Channel Charting as a Service
(2023-2026, CHIST-ERA)
https://chaser-project.github.io/
Channel charting (CC) is an emerging application of self-supervised machine learning to wireless communication which leverages the fact that wireless communications systems continuously collect data about the electromagnetic propagation channel. This data, known as channel state information (CSI), is high-dimensional and acquired at fast rates but typically discarded immediately after use. In contrast, CC recycles acquired CSI data by means of dimensionality reduction to learn a so-called channel chart. This channel chart is essentially a low-dimensional representation of the CSI with the salient property that users who are close in the channel chart are also close in physical space. Put simply: CC is a method that produces a pseudo-location with no recourse to classical positioning methods, potentially opening up a range of location-based applications to operate with significantly reduced overhead. The objective of CHASER is to develop methods and algorithms allowing to implement network-wide CC, and to develop its predictive capabilities when applied to real-world use cases involving multiple base stations or access points, heterogeneous users and dynamically changing environments, with the ultimate goals of developing CC into a robust and versatile pseudo-positioning method to assists a number of network functions and user-level applications.
