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Proceedings ArticleDOI

2D-FFT Based Modulation Classification Using Deep Convolution Neural Network

10 Dec 2020-

AbstractAutomatic 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 channel

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Journal ArticleDOI
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.
Abstract: The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information and so on, blind identification of the modulation is a difficult task. This becomes even more challenging in real-world scenarios with multipath fading, frequency-selective and time-varying channels. With this in mind, the authors provide a comprehensive survey of different modulation recognition techniques in a systematic way. A unified notation is used to bring in together, under the same umbrella, the vast amount of results and classifiers, developed for different modulations. The two general classes of automatic modulation identification algorithms are discussed in detail, which rely on the likelihood function and features of the received signal, respectively. The contributions of numerous articles are summarised in compact forms. This helps the reader to see the main characteristics of each technique. However, in many cases, the results reported in the literature have been obtained under different conditions. So, we have also simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies. Furthermore, new problems that have appeared as a result of emerging wireless technologies are outlined. Finally, open problems and possible directions for future research are briefly discussed.

1,030 citations

Journal ArticleDOI
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.

232 citations

Journal ArticleDOI
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.
Abstract: Automatic Modulation Classification (AMC) is an intermediate step between signal detection and demodulation. It is a very important process for a receiver that has no, or limited, knowledge of received signals. It is important for many areas such as spectrum management, interference identification and for various other civilian and military applications. This paper explores the use of Genetic Programming (GP) in combination with K-nearest neighbor (KNN) for AMC. KNN has been used to evaluate fitness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classification performance of the best individual produced by GP. Four modulation types are considered here: BPSK, QPSK, QAM16 and QAM64. Cumulants have been used as input features for GP. The classification process has been divided into two-stages for improving the classification accuracy. Simulation results demonstrate that the proposed method provides better classification performance compared to other recent methods.

209 citations

Proceedings ArticleDOI
13 Oct 2003
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.
Abstract: In this paper we investigate automatic modulation classification (AMC) using homogeneous feature-vectors based on cyclic cumulants (CCs) of fourth-, sixth- and eight-orders, respectively, for QAM, PSK and ASK signals within a pattern recognition framework. Analysis of CCs of the baseband signal at the receiver is performed and used for feature selection. The cycle spectrum of the baseband signal at the receiver is derived as a function of excess bandwidth for a raised cosine pulse shape and a necessary and sufficient condition on the oversampling factor is obtained. Theoretical arguments regarding the discrimination capability of the examined feature-vectors are verified through extensive simulations.

201 citations

Journal ArticleDOI
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.
Abstract: Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. In this work we propose to use constellation shape as a robust signature for digital modulation recognition. We represent the transmitted “information” by the geometry of the constellation. Received information is in turn the recovered constellation shape that is deformed by noise, channel and receiver implementation. We first demonstrate that fuzzy c-means clustering is capable of robust recovery of the unknown constellation. To perform Bayesian inference, the reconstructed constellation is modeled by a discrete multiple-valued nonhomogenous spatial random field. For candidate modulations, their corresponding random fields are modeled off-line. The unknown constellation shape is then classified by an ML rule based on the preceding model building phase. The algorithm is applicable to digital modulations of arbitrary size and dimensionality.

171 citations