@inproceedings{ef3612c2dd314fb19416ecb401da1749,
title = "Overview of the Urban Wireless Localization Competition",
abstract = "In dense urban environments, Global Navigation Satellite Systems do not provide good accuracy due to the low probability of line-of-sight (LOS) between the user equipment (UE) to be located and the satellites due to the presence of obstacles such as buildings. As a result, it is necessary to resort to other technologies that can operate reliably under non-line-of-sight (NLOS) conditions. To promote research in the reviving field of radio map-based wireless localization, we have launched the MLSP 2023 Urban Wireless Localization Competition. In this short overview paper, we describe the urban wireless localization problem, the provided datasets and baseline methods, the challenge task, and the challenge evaluation methodology. Finally, we present the results of the challenge.",
keywords = "challenge, deep learning, radio map, received signal strength (RSS), time of arrival (ToA), wireless localization",
author = "Cagkan Yapar and Fabian Jaensch and Ron Levie and Gitta Kutyniok and Giuseppe Caire",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023 ; Conference date: 17-09-2023 Through 20-09-2023",
year = "2023",
doi = "10.1109/MLSP55844.2023.10285961",
language = "الإنجليزيّة",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
editor = "Danilo Comminiello and Michele Scarpiniti",
booktitle = "Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing, MLSP 2023",
}