Publication detail

Feature-Based Classification of Digital Modulations Using Various Learning Algorithms

KUBÁNKOVÁ, A. BURGET, R. KUBÁNEK, D. GANIYEV, A.

Original Title

Feature-Based Classification of Digital Modulations Using Various Learning Algorithms

English Title

Feature-Based Classification of Digital Modulations Using Various Learning Algorithms

Type

conference paper

Language

en

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.

English 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

RIV year

2011

Released

07.09.2011

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

4

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",
  annote="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.",
  address="Brno University of Technology",
  booktitle="The 13th International Conference on Research in Telecommunication Technologies RTT - 2011",
  chapter="75345",
  howpublished="electronic, physical medium",
  institution="Brno University of Technology",
  year="2011",
  month="september",
  pages="1--4",
  publisher="Brno University of Technology",
  type="conference paper"
}