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  • The paper deals with classification of digital modulations by means of ten characteristic features of modulated signal and four learning algorithms, namely Artificial Neural Networks, Support Vector Machines, k-Nearest neighbors, and Random Forests. 2ASK, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK, and 16QAM modulations were chosen for classification. Testing of the methods was carried out by simulation with signals disturbed by multipath fading and additive white Gaussian noise. It was found out that the Random Forests algorithm provides best results with over 99 % accuracy.
  • The paper deals with classification of digital modulations by means of ten characteristic features of modulated signal and four learning algorithms, namely Artificial Neural Networks, Support Vector Machines, k-Nearest neighbors, and Random Forests. 2ASK, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK, and 16QAM modulations were chosen for classification. Testing of the methods was carried out by simulation with signals disturbed by multipath fading and additive white Gaussian noise. It was found out that the Random Forests algorithm provides best results with over 99 % accuracy. (en)
Title
  • Feature-Based Classification of Digital Modulations Using Various Learning Algorithms
  • Feature-Based Classification of Digital Modulations Using Various Learning Algorithms (en)
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  • Feature-Based Classification of Digital Modulations Using Various Learning Algorithms
  • Feature-Based Classification of Digital Modulations Using Various Learning Algorithms (en)
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  • RIV/00216305:26220/11:PU95262!RIV12-MSM-26220___
http://linked.open...avai/riv/aktivita
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  • P(GP102/09/P626), S, Z(MSM0021630513)
http://linked.open...vai/riv/dodaniDat
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  • 199467
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  • RIV/00216305:26220/11:PU95262
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  • classification of digital modulations, features, machine learning algorithm (en)
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  • [F1AF460F48BD]
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  • Těchov
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  • Brno, Czech Republic
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  • The 13th International Conference on Research in Telecommunication Technologies RTT - 2011
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  • Burget, Radim
  • Kubánek, David
  • Kubánková, Anna
  • Ganiyev, Artem
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number of pages
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  • Vysoké učení technické v Brně
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  • 978-80-214-4283-2
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  • 26220
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