Blind Transmitter Localization Using Deep Learning: A Scalability Study

Ivo Bizon, Ahmad Nimr, Philipp Schulz, Marwa Chafii, Gerhard P. Fettweis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665491228
DOIs
StatePublished - 2023
Event2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Glasgow, United Kingdom
Duration: Mar 26 2023Mar 29 2023

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2023-March
ISSN (Print)1525-3511

Conference

Conference2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
Country/TerritoryUnited Kingdom
CityGlasgow
Period3/26/233/29/23

Keywords

  • Multi transmitter localization
  • deep learning, positioning
  • network-side localization
  • received signal strength
  • wireless sensor network

ASJC Scopus subject areas

  • General Engineering

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