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Spectrum Prediction and Interference Detection for Satellite Communications
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Spectrum monitoring and interference detection are crucial for the satellite service performance and the revenue of SatCom operators. Interference is one of the major causes of service degradation and deficient operational efficiency. Moreover, the satellite spectrum is becoming more crowded, as more satellites are being launched for different applications. This increases the risk of interference, which causes anomalies in the received signal, and mandates the adoption of techniques that can enable the automatic and real-time detection of such anomalies as a first step towards interference mitigation and suppression.

In this paper, we present a Machine Learning (ML)-based approach able to guarantee a real-time and automatic detection of both short-term and long-term interference in the spectrum of the received signal at the base station. The proposed approach can localize the interference both in time and in frequency and is universally applicable across a discrete set of different signal spectra. We present experimental results obtained by applying our method to real spectrum data from the Swedish Space Corporation. We also compare our ML-based approach to a model-based approach applied to the same spectrum data and used as a realistic baseline. Experimental results show that our method is a more reliable interference detector.

Place, publisher, year, edition, pages
2019.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-261356OAI: oai:DiVA.org:kth-261356DiVA, id: diva2:1357736
Conference
37th International Communications Satellite Systems Conference
Note

QC 20191028

Available from: 2019-10-04 Created: 2019-10-04 Last updated: 2019-10-28Bibliographically approved

Open Access in DiVA

fulltext(1145 kB)10 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf