This repository contains several implementations of Deep Indoor Channel Charting techniques, developed during a personal research project aa part of my Master in Communications and Systems Engineering at Denmark's Technical University (DTU).
Explore the application of Deep Learning in Indoor Channel Charting. Initially I we implemented supervised and unsupervised algorithm to perform Channel Charting. We also explored some simple semi supervised algorithms, that make use of easily attainable unlabeled data.
We include the implementations of:
- Supervised Classifier for predicting in which slice of the given space a transmitter resides.
- A Supervised Regressor for predicting the exact location of the trasmitter
- An Unsupervised Autoencoder to learn a low dimensional embedding to be used as a "map".
- A SemiSupervised Classifier performing almost as well as 2) with 10% of labeled data.
Try to learn the low dimensional mapping using two losses and mixing 1) and 3).
Copyright (c) 2020 Evangelos Stavropoulos
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