Blind Transmitter Localization Using Deep Learning: A Scalability Study

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

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

Original languageEnglish
Title of host publication2023 IEEE Wireless Communications and Networking Conference (WCNC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (electronic)978-1-6654-9122-8
ISBN (print)978-1-6654-9123-5
Publication statusPublished - 29 Mar 2023
Peer-reviewedYes

Publication series

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

Conference

Title2023 IEEE Wireless Communications and Networking Conference
SubtitleWireless Communications for Social Innovation
Abbreviated titleWCNC 2023
Duration26 - 29 March 2023
Website
LocationScottish Event Campus (SEC)
CityGlasgow
CountryUnited Kingdom

External IDs

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

Keywords

ASJC Scopus subject areas

Keywords

  • 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