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KUBÁNKOVÁ, A. BURGET, R. KUBÁNEK, D. GANIYEV, A.
Original Title
Feature-Based Classification of Digital Modulations Using Various Learning Algorithms
Type
conference paper
Language
English
Original Abstract
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.
Keywords
classification of digital modulations; features; machine learning algorithm
Authors
KUBÁNKOVÁ, A.; BURGET, R.; KUBÁNEK, D.; GANIYEV, A.
RIV year
2011
Released
7. 9. 2011
Publisher
Brno University of Technology
Location
Brno, Czech Republic
ISBN
978-80-214-4283-2
Book
The 13th International Conference on Research in Telecommunication Technologies RTT - 2011
Pages from
1
Pages to
4
Pages count
BibTex
@inproceedings{BUT75345, author="Anna {Kubánková} and Radim {Burget} and David {Kubánek} and Artem {Ganiyev}", title="Feature-Based Classification of Digital Modulations Using Various Learning Algorithms", booktitle="The 13th International Conference on Research in Telecommunication Technologies RTT - 2011", year="2011", pages="1--4", publisher="Brno University of Technology", address="Brno, Czech Republic", isbn="978-80-214-4283-2" }