Signal Detection and Modulation Classification for Satellite Communications

Veronica Toro Betancur; Augusto Carmona Valencia; José Ignacio Marulanda Bernal

doi:10.1145/3432291.3432297

Signal Detection and Modulation Classification for Satellite Communications

Veronica Toro Betancur, Augusto Carmona Valencia, José Ignacio Marulanda Bernal

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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Abstract

Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolute-valued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks.

Original language	English
Title of host publication	Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning
Place of Publication	New York, NY, USA
Publisher	Association for Computing Machinery (ACM)
Pages	114–118
Number of pages	5
ISBN (Electronic)	9781450375733
DOIs	https://doi.org/10.1145/3432291.3432297
Publication status	Published - 22 Oct 2020
MoE publication type	A4 Article in a conference publication
Event	International Conference on Signal Processing and Machine Learning - Online, Beijing, China Duration: 22 Oct 2020 → 24 Oct 2020 Conference number: 3 http://www.spml.net/2020.html

Conference

Conference	International Conference on Signal Processing and Machine Learning
Abbreviated title	SPML
Country	China
City	Beijing
Period	22/10/2020 → 24/10/2020
Internet address	http://www.spml.net/2020.html

Keywords

Modulation classification
signal detection
Spectrum sensing
Support vector machines
Artificial neural networks
Amateur Satellites

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Toro Betancur, Veronica (Recipient), 24 Oct 2020
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title = "Signal Detection and Modulation Classification for Satellite Communications",

abstract = "Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolute-valued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks.",

keywords = "Modulation classification, signal detection, Spectrum sensing, Support vector machines, Artificial neural networks, Amateur Satellites",

author = "{Toro Betancur}, Veronica and {Carmona Valencia}, Augusto and {Marulanda Bernal}, {Jos{\'e} Ignacio}",

year = "2020",

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doi = "10.1145/3432291.3432297",

language = "English",

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booktitle = "Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning",

publisher = "Association for Computing Machinery (ACM)",

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note = "International Conference on Signal Processing and Machine Learning, SPML ; Conference date: 22-10-2020 Through 24-10-2020",

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}

Toro Betancur, V, Carmona Valencia, A & Marulanda Bernal, JI 2020, Signal Detection and Modulation Classification for Satellite Communications. in Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning. Association for Computing Machinery (ACM), New York, NY, USA, pp. 114–118, International Conference on Signal Processing and Machine Learning, Beijing, China, 22/10/2020. https://doi.org/10.1145/3432291.3432297

Signal Detection and Modulation Classification for Satellite Communications. / Toro Betancur, Veronica; Carmona Valencia, Augusto; Marulanda Bernal, José Ignacio.

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning. New York, NY, USA : Association for Computing Machinery (ACM), 2020. p. 114–118.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Signal Detection and Modulation Classification for Satellite Communications

AU - Toro Betancur, Veronica

AU - Carmona Valencia, Augusto

AU - Marulanda Bernal, José Ignacio

N1 - Conference code: 3

PY - 2020/10/22

Y1 - 2020/10/22

N2 - Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolute-valued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks.

AB - Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolute-valued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks.

KW - Modulation classification

KW - signal detection

KW - Spectrum sensing

KW - Support vector machines

KW - Artificial neural networks

KW - Amateur Satellites

U2 - 10.1145/3432291.3432297

DO - 10.1145/3432291.3432297

M3 - Conference contribution

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EP - 118

BT - Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning

PB - Association for Computing Machinery (ACM)

CY - New York, NY, USA

T2 - International Conference on Signal Processing and Machine Learning

Y2 - 22 October 2020 through 24 October 2020

ER -

Signal Detection and Modulation Classification for Satellite Communications

Abstract

Conference

Keywords

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