Support vector machine based classifier for digital modulations in presence of HF noise

Turky N. AlOtaiby; Mobien Shoaib; Alshebeili Saleh; Alharbi Hazza

doi:10.1109/SIECPC.2013.6551016

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Conference proceeding

Support vector machine based classifier for digital modulations in presence of HF noise

Turky N. AlOtaiby, Mobien Shoaib, Alshebeili Saleh and Alharbi Hazza

2013 Saudi International Electronics, Communications and Photonics Conference, pp.1-4

04/2013

DOI: https://doi.org/10.1109/SIECPC.2013.6551016

Abstract

Automatic Modulation Classification (AMC)

Bi-kappa noise

Feature extraction

Feature-Based Classification

Frequency shift keying

HF communications

Signal to noise ratio

Support vector machines

Training

Support vector machines (SVMs) deal with challenging classification problems. One such a challenge is to classify the modulation type of a signal transmitted over High frequency (HF) band. The noise distribution in this band has time varying nature. SVM can mitigate these variations with correct choice of features and kernel functions. This paper presents a feature-based classification method utilizing SVM for the classification of 10 types of modulations in the presence of Gaussian as well as non-Gaussian noise disturbances. The proposed method is able to classify type and order of modulation at relatively low signal-to-noise ratios (SNRs) for both simulated as well as actual data.

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Title: Support vector machine based classifier for digital modulations in presence of HF noise
Creators - without role: Turky N. AlOtaiby - King Saud University
Mobien Shoaib - King Saud University
Alshebeili Saleh - King Saud University
Alharbi Hazza - King Saud University
Publication Details: 2013 Saudi International Electronics, Communications and Photonics Conference, pp.1-4
Publisher: IEEE
Identifiers: 9919004508331
Academic Unit: King Abdulaziz City for Science & Technology; King Saud University
Language: English
Resource Type: Conference proceeding