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Conference Papers Year : 2016

Neural Network Classification of SDR Signal Modulation

Jakub Stebel
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  • PersonId : 1023039
Michal Krumnikl
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  • PersonId : 1023007
Pavel Moravec
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  • PersonId : 1009858
Petr Olivka
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  • PersonId : 1023008
David Seidl
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Abstract

With the rising popularity of Software Defined Radios (SDR), there is a strong demand for automatic detection of the modulation type and signal parameters. Automatic modulation classification is an approach to identify the modulation type and its parameters such as the carrier frequency or symbol rate. In electronic warfare, it enables real-time signal interception and processing. In civil applications, it can be used, e.g., by the amateur radio operators to automatically set the transceiver to the appropriate modulation and communication protocol. This paper presents a modulation classification driven by a neural network. A set of signal features are provided as an input of the neural network. The paper discusses the relevance of different signal features and its impact on the success rate of the neural network classification. The proposed approach is tested on both artificial and real samples captured by the SDR.
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hal-01637483 , version 1 (17-11-2017)

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Jakub Stebel, Michal Krumnikl, Pavel Moravec, Petr Olivka, David Seidl. Neural Network Classification of SDR Signal Modulation. 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. pp.160-171, ⟨10.1007/978-3-319-45378-1_15⟩. ⟨hal-01637483⟩
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