Title: Automatic modulation recognition for DVB-S2 using pairwise support vector machines

Authors: Mohsen Farhang; Ali Ghaleh; Hamid Dehghani

Addresses: Malek-Ashtar University of Technology, Lavizan, Tehran, Iran ' Qazvin Azad University, Barajin Rd, Barajin, Qazvin, Iran ' Malek-Ashtar University of Technology, Lavizan, Tehran, Iran

Abstract: In this paper, a support vector machine (SVM) pairwise coupling algorithm is developed for classification of satellite communications signals used in second generation of digital video broadcasting via satellite (DVB-S2) standard. DVB-S2 standard adaptively uses one of QPSK, 8PSK, 16APSK, and 32APSK modulations. The proposed method extracts fourth and sixth order cumulants as features from the received signal. The features are given to a SVM pairwise coupling algorithm in which there is one binary SVM for each pair of modulation types. Finally the algorithm selects the modulation type chosen by the maximal number of pairwise SVMs as final decision. SVMs are trained by samples from different modulation types corrupted by Gaussian noise. The simulation results show that the proposed method allows higher recognition rates in comparison with previous methods, especially at low SNRs.

Keywords: automatic modulation classification; AMC; pairwise support vector machine; cumulant; digital video broadcasting via satellite; DVB-S2.

DOI: 10.1504/IJAACS.2018.092020

International Journal of Autonomous and Adaptive Communications Systems, 2018 Vol.11 No.2, pp.144 - 153

Received: 11 Jun 2015
Accepted: 10 Feb 2016

Published online: 22 May 2018 *

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