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

• URL: http://arxiv.org/abs/2204.13996v1
• Date: Mon, 4 Apr 2022 12:01:36 GMT
• Title: Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting
• Authors: Taha Yassine (IRT b-com, INSA Rennes), Luc Le Magoarou (IRT b-com), St\'ephane Paquelet (IRT b-com), Matthieu Crussi\`ere (IRT b-com, IETR, INSA Rennes)
• Abstract summary: Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart. In this paper, a model-based deep learning approach to this problem is proposed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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