Projekter pr. år
Abstract
It can be extremely mission-critical to identify the source of an error in satellite communication systems and in this initial work an approach based on neural networks is proposed for diagnosing the faulty antenna elements remotely from the receiver side. A simple active antenna array which consists of four power amplifiers and a four-by-one linear antenna array has been considered in this work for proof of concept. Signals captured from various fault scenarios are used to train a 4layered feed-forward neural network using amplitude and phase data. The validation results show that the trained neural network can correctly identify the fault power amplifier with an accuracy higher than 96 %, which indicates the promising potential of the proposed approach.
Originalsprog | Engelsk |
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Titel | 2019 27th Telecommunications Forum (TELFOR) |
Antal sider | 4 |
Forlag | IEEE (Institute of Electrical and Electronics Engineers) |
Publikationsdato | 2020 |
Artikelnummer | 8971339 |
ISBN (Trykt) | 978-1-7281-4790-1 |
ISBN (Elektronisk) | 978-1-7281-4790-1 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | 2019 27th Telecommunications Forum (TELFOR) - Beograd, Serbien Varighed: 26 nov. 2019 → 27 nov. 2019 |
Konference
Konference | 2019 27th Telecommunications Forum (TELFOR) |
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Land/Område | Serbien |
By | Beograd |
Periode | 26/11/2019 → 27/11/2019 |
Navn | Proceedings of the IEEE Telecommunications Forum (TELFOR) |
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Emneord
- Machine learning
- Satellite communications
- Neural Network
- Antenna array
- Antennas
Fingeraftryk
Dyk ned i forskningsemnerne om 'Remote Diagnosis of Fault Element in Active Phased Arrays using Deep Neural Network'. Sammen danner de et unikt fingeraftryk.Projekter
- 1 Afsluttet
-
Deep Learning Based Communication for Power-Efficient Satellite Systems
Shen, M. (PI (principal investigator)) & De Carvalho, E. (CoPI)
15/09/2020 → 14/09/2023
Projekter: Projekt › Forskning