Proceedings ArticleDOI
2D-FFT Based Modulation Classification Using Deep Convolution Neural Network
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TLDR
In this paper, a convolution neural network (CNN) based automatic modulation classification (AMC) method is proposed, where two dimensional Fast Fourier Transform (2D-FFT) is used as a classification feature and a less complex and efficient deep CNN model is designed to classify the modulation schemes of different orders of PSK and QAM.Abstract:
Automatic modulation classification (AMC) has a wide range of applications in the military and civilian areas In the military, it is used for the extraction of information from unknown intercepted signals and the generation of jamming signals Civil applications include interference management and spectrum underutilization To overcome the limitations of traditional methods like maximum likelihood (ML) and feature- based (FB), deep learning (DL) networks have been developed and are being evolved Following this direction, a convolution neural network (CNN) based AMC method is proposed The two dimensional Fast Fourier Transform (2D-FFT) is used as a classification feature and a less complex and efficient deep CNN model is designed to classify the modulation schemes of different orders of PSK and QAM The developed method achieves adequate classification performance for considered five modulation schemes in the AWGN channelread more
Citations
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Blind Signal Modulation Classification Using Constellation Pattern Analysis with Oversampling Factor Alteration
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Image-based material analysis of ancient historical documents
TL;DR: This study uses images of a famous historical collection, the Dead Sea Scrolls, to propose a novel method to classify the materials of the manuscripts using the two-dimensional Fourier Transform to identify patterns within the manuscript surfaces.
References
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Proceedings ArticleDOI
Naïve Bayes classification of adaptive broadband wireless modulation schemes with higher order cumulants
TL;DR: This work evaluates some higher order statistical measures coupled with a classical Naive Bayes classifier for fast identification of adaptive modulation schemes and benchmark the experimental results with the optimal Maximum Likelihood Classifier, and Support Vector Machine based Classifier using the same feature set.
Journal ArticleDOI
Blind Signal PSK/QAM Recognition Using Clustering Analysis of Constellation Signature in Flat Fading Channel
TL;DR: Results show that the proposed algorithm outperforms some existing classifiers and offers lower computational complexity compared to algorithms based on subtractive clustering.
Proceedings ArticleDOI
Automatic modulation classification: Sixth-order cumulant features as a solution for real-world challenges
TL;DR: Results of theoretical analysis of different properties of algorithm are presented, along with new channel estimation algorithm, proposed in literature for adoption within the system with automatic modulation classification, and methods for improvement of algorithm's performance under specific propagation conditions are presented.
Proceedings ArticleDOI
An algorithm for blind symbol rate estimation using second order cyclostationarity
TL;DR: In this article, an alternative blind approach using the cyclic domain energy profile is proposed to minimize the effects of channel conditions and parameter uncertainty, which shows better performance in the low SNR region with no susceptibility to the pulse shape variation and multipath fading.
Journal ArticleDOI
Low-complexity cyclostationary-based modulation classifying algorithm
TL;DR: A low-complexity cyclostationary-based modulation classifier is presented, which is capable of distinguishing between OFDM, GFSK and QPSK modulations, allowing a real-time hardware implementation of the algorithms at a limited cost.