AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO

An Automatic Modulation Classification (AMC) system for Software Defined Radio (SDR) is presented in this study. Initially, the generated signals are modulated using different modulation techniques. Then, noise is added to the generated signals by using Additive White Gaussian Noise (AWGN). The nois...

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Main Author: Hasin Alam
Format: Article
Language:English
Published: XLESCIENCE 2018-12-01
Series:International Journal of Advances in Signal and Image Sciences
Subjects:
Online Access:https://xlescience.org/index.php/IJASIS/article/view/33
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author Hasin Alam
author_facet Hasin Alam
author_sort Hasin Alam
collection DOAJ
description An Automatic Modulation Classification (AMC) system for Software Defined Radio (SDR) is presented in this study. Initially, the generated signals are modulated using different modulation techniques. Then, noise is added to the generated signals by using Additive White Gaussian Noise (AWGN). The noise added signal is used for further process to extract features and classification. The system uses Discrete Wavelet Transform (DWT) to analyze the signal that produces lower and higher frequency sub-bands. The Independent Component Analysis (ICA) is employed on lower frequency sub-band for dimensionality reduction. Finally, the classification is made by Pulse Coupled Neural Network (PCNN). The system uses three different digital modulation schemes; Phase Shift Keying (PSK), Quadrature Amplitude Modulation (QAM), and Differential PSK (DPSK). The results show the DWT, ICA and PCNN based AMC system provides promising results under various noise densities.
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spelling doaj.art-8abb445e0f2e459c9b0720791b5e3b412022-12-22T00:15:53ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702018-12-01421710.29284/ijasis.4.2.2018.1-733AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIOHasin AlamAn Automatic Modulation Classification (AMC) system for Software Defined Radio (SDR) is presented in this study. Initially, the generated signals are modulated using different modulation techniques. Then, noise is added to the generated signals by using Additive White Gaussian Noise (AWGN). The noise added signal is used for further process to extract features and classification. The system uses Discrete Wavelet Transform (DWT) to analyze the signal that produces lower and higher frequency sub-bands. The Independent Component Analysis (ICA) is employed on lower frequency sub-band for dimensionality reduction. Finally, the classification is made by Pulse Coupled Neural Network (PCNN). The system uses three different digital modulation schemes; Phase Shift Keying (PSK), Quadrature Amplitude Modulation (QAM), and Differential PSK (DPSK). The results show the DWT, ICA and PCNN based AMC system provides promising results under various noise densities.https://xlescience.org/index.php/IJASIS/article/view/33automated modulation classification, machine learning algorithms, dimensionality reduction techniques, software defined radio
spellingShingle Hasin Alam
AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO
International Journal of Advances in Signal and Image Sciences
automated modulation classification, machine learning algorithms, dimensionality reduction techniques, software defined radio
title AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO
title_full AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO
title_fullStr AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO
title_full_unstemmed AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO
title_short AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO
title_sort automated modulation classification system for software defined radio
topic automated modulation classification, machine learning algorithms, dimensionality reduction techniques, software defined radio
url https://xlescience.org/index.php/IJASIS/article/view/33
work_keys_str_mv AT hasinalam automatedmodulationclassificationsystemforsoftwaredefinedradio