Blind Transmitter Localization Using Deep Learning: A Scalability Study

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

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.

Details

OriginalspracheEnglisch
Titel2023 IEEE Wireless Communications and Networking Conference (WCNC)
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers (IEEE)
Seiten1-6
Seitenumfang6
ISBN (elektronisch)978-1-6654-9122-8
ISBN (Print)978-1-6654-9123-5
PublikationsstatusVeröffentlicht - 29 März 2023
Peer-Review-StatusJa

Publikationsreihe

ReiheIEEE Conference on Wireless Communications and Networking (WCNC)
ISSN1525-3511

Konferenz

Titel2023 IEEE Wireless Communications and Networking Conference
UntertitelWireless Communications for Social Innovation
KurztitelWCNC 2023
Dauer26 - 29 März 2023
Webseite
OrtScottish Event Campus (SEC)
StadtGlasgow
LandGroßbritannien/Vereinigtes Königreich

Externe IDs

Scopus 85159781848
ORCID /0000-0003-3045-6271/work/197320371
ORCID /0000-0002-0738-556X/work/197320476

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

  • Deep learning, Location awareness, Scalability, Simulation, Transmitters, Wireless communication, Wireless sensor networks, Multi transmitter localization, network-side localization, received signal strength, wireless sensor network, deep learning, positioning