Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting
This work addresses channel charting for wireless communication systems, but it appears incremental as it builds on existing methods with specific optimizations.
The paper tackled the problem of unsupervised mapping of wireless channels to a spatial chart by proposing a model-based deep learning approach that uses a physically motivated distance measure and triplet loss, resulting in a method with low parameters and fast training suitable for on-the-fly applications, yielding encouraging results on synthetic channels.
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.