Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting
Résumé
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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main.pdf (561.39 Ko)
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chart_locs.pdf (212.04 Ko)
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chart_mlp.pdf (19.77 Ko)
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chart_rand.pdf (19.84 Ko)
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chart_smart.pdf (19.79 Ko)
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hybrid_cc.eps_tex (2.15 Ko)
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lineplot.pdf (119.32 Ko)
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triplet_dataset.png (47.92 Ko)
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Origine : Fichiers produits par l'(les) auteur(s)
Origine : Fichiers produits par l'(les) auteur(s)