Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper, a model-based deep learning approach to this problem is proposed. It builds on a physically motivated distance measure to structure and initialize a neural network that is subsequently trained using a triplet loss function. The proposed structure exhibits a low number of parameters and clever initialization leads to fast training. These two features make the proposed approach amenable to on-the-fly channel charting. The method is empirically assessed on realistic synthetic channels, yielding encouraging results.
@article{arxiv.2204.13996,
title = {Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting},
author = {Taha Yassine and Luc Le Magoarou and Stéphane Paquelet and Matthieu Crussière},
journal= {arXiv preprint arXiv:2204.13996},
year = {2022}
}