Proceedings ArticleDOI
2D-FFT Based Modulation Classification Using Deep Convolution Neural Network
Reads0
Chats0
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
More filters
Journal ArticleDOI
Blind Signal Modulation Classification Using Constellation Pattern Analysis with Oversampling Factor Alteration
Proceedings ArticleDOI
Image-based material analysis of ancient historical documents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
More filters
Journal ArticleDOI
Survey of automatic modulation classification techniques: classical approaches and new trends
TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
Journal ArticleDOI
Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels
Abstract: Nowadays, automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. Very often, multipath fading channels result in the severe AMC performance degradation or induce large classification errors. The negative impacts of multipath fading channels on AMC have been discussed in the existing literature but no solution has ever been proposed so far to the best of our knowledge. In this paper, we propose a new robust AMC algorithm, which applies higher-order statistics (HOS) in a generic framework for blind channel estimation and pattern recognition. We also derive the Cramer-Rao lower bound for the fourth-order cumulant estimator when the AMC candidates are BPSK and QPSK over the additive white Gaussian noise channel, and it is a nearly minimum-variance estimator leading to robust AMC features in a wide variety of signal-to-noise ratios. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we do not really have to acquire the complete channel information and therefore it can be feasible without any a priori information in practice. The Monte Carlo simulation results show that our new AMC algorithm can achieve the much better classification accuracy than the existing AMC techniques.
Journal ArticleDOI
Automatic Modulation Classification Using Combination of Genetic Programming and KNN
TL;DR: This paper explores the use of Genetic Programming in combination with K-nearest neighbor (KNN) for AMC and demonstrates that the proposed method provides better classification performance compared to other recent methods.
Proceedings ArticleDOI
Higher-order cyclic cumulants for high order modulation classification
TL;DR: This paper investigates automatic modulation classification (AMC) using homogeneous feature-vectors based on cyclic cumulants of fourth, sixth- and eight-orders for QAM, PSK and ASK signals within a pattern recognition framework.
Journal ArticleDOI
Digital modulation classification using constellation shape
TL;DR: This work demonstrates that fuzzy c-means clustering is capable of robust recovery of the unknown constellation and proposes to use constellation shape as a robust signature for digital modulation recognition.