TY - GEN
T1 - Blind Transmitter Localization Using Deep Learning
T2 - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
AU - Bizon, Ivo
AU - Nimr, Ahmad
AU - Schulz, Philipp
AU - Chafii, Marwa
AU - Fettweis, Gerhard P.
N1 - Funding Information:
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) through the projects ”Industrial Radio Lab Germany (IRLG)” under contract 16KIS1010K and ”6G-life” under contract 16KISK001K. We also thank the Center for Information Services and High Performance Computing (ZIH) at TU Dresden.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.
AB - This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.
KW - Multi transmitter localization
KW - deep learning, positioning
KW - network-side localization
KW - received signal strength
KW - wireless sensor network
UR - http://www.scopus.com/inward/record.url?scp=85159781848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159781848&partnerID=8YFLogxK
U2 - 10.1109/WCNC55385.2023.10119115
DO - 10.1109/WCNC55385.2023.10119115
M3 - Conference contribution
AN - SCOPUS:85159781848
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 March 2023 through 29 March 2023
ER -