Blind Transmitter Localization Using Deep Learning: A Scalability Study
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-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 language | English |
|---|---|
| Title of host publication | 2023 IEEE Wireless Communications and Networking Conference (WCNC) |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (electronic) | 978-1-6654-9122-8 |
| ISBN (print) | 978-1-6654-9123-5 |
| Publication status | Published - 29 Mar 2023 |
| Peer-reviewed | Yes |
Publication series
| Series | IEEE Conference on Wireless Communications and Networking (WCNC) |
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| ISSN | 1525-3511 |
Conference
| Title | 2023 IEEE Wireless Communications and Networking Conference |
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| Subtitle | Wireless Communications for Social Innovation |
| Abbreviated title | WCNC 2023 |
| Duration | 26 - 29 March 2023 |
| Website | |
| Location | Scottish Event Campus (SEC) |
| City | Glasgow |
| Country | United 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