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Talks and Poster Presentations (with Proceedings-Entry):

A. Salihu, S. Schwarz, A. Pikrakis, M. Rupp:
"Low-dimensional Representation Learning for Wireless CSI-based Localisation";
Talk: IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020), Thessaloniki, Greece; 10-12-2020 - 10-14-2020; in: "Proceedings of International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2020)", IEEE, (2020).



English abstract:
In this work, we investigate the potential of deep feedforward neural networks for user position estimation for a multi-path directional channel model in presence, as well as absence of the line of sight path. Furthermore, we take advantage of a triplet network architecture combined with a triplet loss function, which allows us to exploit intrinsic properties of channel state information. We show that despite the high-dimensional nature of CSI, the proposed network can be trained to learn from the low-dimensional space and with less amount of training samples compared to the long-established approaches in the literature for fingerprinting localization. Finally, in order to investigate the performance and emphasize the benefits of triplet loss, we compare it to another network based on classification.


Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_289951.pdf


Created from the Publication Database of the Vienna University of Technology.