Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting

Abstract

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.

Taha Yassine

Phd student in Artificial Intelligence and Wireless Communications

My current research topics include signal processing, wireless communications and machine learning.