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Machine learning-based characterization of SNR in digital satellite communication links

Brecht Dhuyvetters, Daniel Delaruelle, Hendrik Rogier (UGent) , Tom Dhaene (UGent) , Dries Vande Ginste (UGent) and Domenico Spina (UGent)
(2021) 2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP). In Proceedings of the European Conference on Antennas and Propagation
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Abstract
Signals traveling through a Satellite Communication (SatCom) channel are subject to noise and interference effects, impacting their Signal-to-Noise ratio (SNR). Furthermore, non-linear distortion arising from the nonlinear characteristic of the amplifiers in the system also adversely impacts performance. Current state-of-the-art techniques estimate these effects by including a sequence of known pilot symbols in the transmitted signals. While robust, a downside of these approaches is that pilot symbols do not include useful information, thus introducing overhead. This paper presents a Machine Learning (ML) approach to characterize the SNR, using the received signal in the return link of SatCom systems, independent of the signal's distortion level and without relying on pilot symbols. The proposed technique is validated through a suitable application example: the characterization of SNR in a SatCom system using a 16-APSK modulation scheme.

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MLA
Dhuyvetters, Brecht, et al. “Machine Learning-Based Characterization of SNR in Digital Satellite Communication Links.” 2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP), IEEE, 2021, doi:10.23919/EuCAP51087.2021.9410971.
APA
Dhuyvetters, B., Delaruelle, D., Rogier, H., Dhaene, T., Vande Ginste, D., & Spina, D. (2021). Machine learning-based characterization of SNR in digital satellite communication links. 2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP). Presented at the EUCAP2021, the 15th European Conference on Antennas and Propagation, Dusseldorf, Germany (Online). https://doi.org/10.23919/EuCAP51087.2021.9410971
Chicago author-date
Dhuyvetters, Brecht, Daniel Delaruelle, Hendrik Rogier, Tom Dhaene, Dries Vande Ginste, and Domenico Spina. 2021. “Machine Learning-Based Characterization of SNR in Digital Satellite Communication Links.” In 2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP). IEEE. https://doi.org/10.23919/EuCAP51087.2021.9410971.
Chicago author-date (all authors)
Dhuyvetters, Brecht, Daniel Delaruelle, Hendrik Rogier, Tom Dhaene, Dries Vande Ginste, and Domenico Spina. 2021. “Machine Learning-Based Characterization of SNR in Digital Satellite Communication Links.” In 2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP). IEEE. doi:10.23919/EuCAP51087.2021.9410971.
Vancouver
1.
Dhuyvetters B, Delaruelle D, Rogier H, Dhaene T, Vande Ginste D, Spina D. Machine learning-based characterization of SNR in digital satellite communication links. In: 2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP). IEEE; 2021.
IEEE
[1]
B. Dhuyvetters, D. Delaruelle, H. Rogier, T. Dhaene, D. Vande Ginste, and D. Spina, “Machine learning-based characterization of SNR in digital satellite communication links,” in 2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP), Dusseldorf, Germany (Online), 2021.
@inproceedings{8701791,
  abstract     = {{Signals traveling through a Satellite Communication (SatCom) channel are subject to noise and interference effects, impacting their Signal-to-Noise ratio (SNR). Furthermore, non-linear distortion arising from the nonlinear characteristic of the amplifiers in the system also adversely impacts performance. Current state-of-the-art techniques estimate these effects by including a sequence of known pilot symbols in the transmitted signals. While robust, a downside of these approaches is that pilot symbols do not include useful information, thus introducing overhead. This paper presents a Machine Learning (ML) approach to characterize the SNR, using the received signal in the return link of SatCom systems, independent of the signal's distortion level and without relying on pilot symbols. The proposed technique is validated through a suitable application example: the characterization of SNR in a SatCom system using a 16-APSK modulation scheme.}},
  author       = {{Dhuyvetters, Brecht and Delaruelle, Daniel and Rogier, Hendrik and Dhaene, Tom and Vande Ginste, Dries and Spina, Domenico}},
  booktitle    = {{2021 15TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP)}},
  isbn         = {{9788831299022}},
  issn         = {{2164-3342}},
  language     = {{eng}},
  location     = {{Dusseldorf, Germany (Online)}},
  pages        = {{5}},
  publisher    = {{IEEE}},
  title        = {{Machine learning-based characterization of SNR in digital satellite communication links}},
  url          = {{http://doi.org/10.23919/EuCAP51087.2021.9410971}},
  year         = {{2021}},
}
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