Remote Diagnosis of Fault Element in Active Phased Arrays using Deep Neural Network

Martin Hedegaard Nielsen, Mads Helge Jespersen, Ming Shen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2019 27th Telecommunications Forum (TELFOR)
Number of pages4
Publication date2020
Article number8971339
ISBN (Print)978-1-7281-4790-1
ISBN (Electronic)978-1-7281-4790-1
Publication statusPublished - 2020
Event2019 27th Telecommunications Forum (TELFOR) - Beograd, Serbia
Duration: 26 Nov 201927 Nov 2019


Conference2019 27th Telecommunications Forum (TELFOR)
SeriesProceedings of the IEEE Telecommunications Forum (TELFOR)


  • Antenna Arrays
  • Antennas
  • Machine learning
  • Neural Network
  • Satellite Communication


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