Neural network classification of SDR signal modulation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F16%3A86098899" target="_blank" >RIV/61989100:27240/16:86098899 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/16:86098899

Výsledek na webu

<a href="http://link.springer.com/content/pdf/10.1007/978-3-319-45378-1_15.pdf" target="_blank" >http://link.springer.com/content/pdf/10.1007/978-3-319-45378-1_15.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-45378-1_15" target="_blank" >10.1007/978-3-319-45378-1_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural network classification of SDR signal modulation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Neural network classification of SDR signal modulation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computer information systems and industrial management : 15th IFIP TC8 International Conference, CISIM 2016 : Vilnius, Lithuania, September 14-16, 2016 : proceedings

ISBN

978-3-319-45377-4

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

160-171

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Vilnius

Datum konání akce

14. 9. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000388720000015