TY - GEN
T1 - Experimental Performance of Blind Position Estimation Using Deep Learning
AU - Bizon, Ivo
AU - Li, Zhongju
AU - Nimr, Ahmad
AU - Chafii, Marwa
AU - Fettweis, Gerhard P.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the European Union’s Horizon 2020 research and innovation programme through the project iNGENIOUS under grant agreement 957216, by the German Research Foundation (DFG, Deutsche Forschungs-gemeinschaft) as part of Germany’s Excellence Strategy - EXC 2050/1 - Project ID 390696704 - Cluster of Excellence ”Centre for Tactile Internet with Human-in-the-Loop” (CeTI) and by the German Federal Ministry of Education and Research (BMBF) (AI4Mobile under grant 16KIS1177, and 6G-life under grant 16KISK001K). We also thank the Center for Information Services and High Performance Computing (ZIH) at TU Dresden.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.
AB - Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.
KW - Blind localization
KW - deep learning
KW - positioning
KW - received signal strength
KW - wireless sensor network
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U2 - 10.1109/GLOBECOM48099.2022.10001103
DO - 10.1109/GLOBECOM48099.2022.10001103
M3 - Conference contribution
AN - SCOPUS:85146954165
T3 - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
SP - 4553
EP - 4557
BT - 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Global Communications Conference, GLOBECOM 2022
Y2 - 4 December 2022 through 8 December 2022
ER -