Modulation recognition based on constellation diagram for M-QAM signals

Zhendong, Chou; Jiang, Weining; Cheng, Xiang; Li, Min

doi:10.1109/icemi.2013.6743041

Cited by 20 publications

(10 citation statements)

References 2 publications

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“…Each location has distinct distance and phase with respect to the origin point and is used by Mobasseri [83] to transfer the phase-amplitude distribution to 1D distribution. In [84,85], the authors adopted constellation features to identify M-QAM. However, the shortcomings of this method were its sensitivity to noise and high SNR requirement in identifying higher-order modulation.…”

Section: Constellation Shape Features For Mrmentioning

confidence: 99%

“…Several common approaches, such as decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), k-nearest neighbor, and combinations of artificial intelligence techniques, have been used for classification. ANN and SVM are supervised machine learning, whereas clustering classifier belongs to unsupervised machine learning [83][84][85]. Researchers have used optimization techniques to select the dominant or significant features from the extracted features for improving the recognition accuracy of classifiers.…”

Section: Overview Of the Type Of Classifiers Used For Mrmentioning

confidence: 99%

“…For instance, assuming a set of animals, such as dog, cat, parrot, sheep, and pigeon, we can create two clusters from the animal data set on the basis of whether the animal can fly or not [84]. Although clustering algorithms are not popular for AMR, few clustering algorithms, such as k-means [85], fuzzy c-means [83], and subtractive clustering [84,105], have been used in the literature. An MQAM MR method was discussed in [106], thereby proposing a cyclic approximation method for estimating the carrier frequency and a method for subtractive clustering with a clustering radius.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

“…This is greatly reduces the computational complexity. In [85], the K-means clustering method, which needs to calculate the number of clustering center points for each hypothesis, was used, leading to an increase of computational complexity. Table 3 presents the summary of the review presented in Sections 4.1-4.5.…”

Section: Complexity Analysis For Classifiersmentioning

confidence: 99%

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

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Section: Constellation Shape Features For Mrmentioning

confidence: 99%

Section: Overview Of the Type Of Classifiers Used For Mrmentioning

confidence: 99%

Section: Clustering Algorithmsmentioning

confidence: 99%

Section: Complexity Analysis For Classifiersmentioning

confidence: 99%

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Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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“…To support this heterogeneity, OPM technology is expected to have modulation format identification function to identify different modulation formats. MFI techniques for optical network include identification based on artificial neural networks (ANN) [6], [7], K-means-based constellation diagram identification technique [8], stokes space-based methods [9], [10] and newly proposed nonlinear power transformation [11], etc. K-means-based constellation diagram identification need ideal constellation diagrams which is hard to acquire under practical circumstances.…”

Section: Introductionmentioning

confidence: 99%

Modulation Format Identification Based on an Improved RBF Neural Network Trained With Asynchronous Amplitude Histogram

Zhou

Huang

et al. 2020

IEEE Access

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This paper proposed a method of modulation format identification using Radial Basis Function Artificial Neural Network (RBF-ANN) trained with Asynchronous Amplitude Histograms (AAHs). Compare with the traditional RBF-ANN, the proposed method is improved by applying Expectation Maximization (EM), which takes advantage of the statistical feature of AAHs, to select center vector for radial basis function. Assuming distribution of each bin in AAH as Gaussian mixture model (GMM), the mean values of the model can be exploited as the center vector which obtained using EM. This approach ensures that the center vector is unbiased and optimal. The center vector is implemented to RBF-ANN to identify different modulation formats. Numerical simulation results demonstrated that identification accuracy was about 99% for three commonly-used modulation formats within the OSNR between 40 ∼ 10dB. And the CD tolerance was 1000ps/nm. In comparison, former center vector selection approaches include K-means and random selection were applied. The result showed that the EM method improved the identification accuracy by 2% to 4% when OSNR = 10dB and CD = 100ps/nm. Owing to its excellent performance, this method can be employed in the next generation optical transport network for auto-adaption modulation format identification. INDEX TERMS Modulation format identification, artificial neural network, asynchronous amplitude histogram, expectation maximization.

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An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

Abdel‐Moneim

Al‐Makhlasawy

Bauomy

et al. 2022

Trans Emerging Tel Tech

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Recently, automatic modulation classification (AMC) has extensively and commonly been utilized in several modern wireless communication systems as a significant tool of signal detection for civilian and military applications and cognitive radio systems. Although several methods have been established to identify the modulation scheme of a received signal, they show a difficulty of learning radio characteristics for most conventional machine learning algorithms. This article focuses on the deep learning (DL) classification technique to solve these problems. To improve the classification accuracy of a communication signal modulation type, we apply a new model that combines Gabor filtering and thresholding with the help of convolution filters implemented in DL. A basic convolutional neural network, AlexNet, and a residual neural network are used for being compatible with constellation diagrams in order to achieve a superior classification performance. Moreover, the Gabor filter can effectively extract spatial information, including edges and textures. In terms of classification accuracy, the proposed AMC system improves the signal modulation classification accuracy significantly, and achieves competitive results. We use seven modulation types over the range of signal-noise ratio (SNR) values from −10 to 30 dB. The performed experiments reveal that the proposal guarantees a remarkable classification accuracy of approximately 100% at a 10 dB SNR over AWGN and Rayleigh fading channels. Therefore, to prove the functional viability of our proposed method, it can be applied in adaptive modulators that can be used in many devices in applications such as Internet-of-Things (IoT). INTRODUCTIONDue to the increasingly growing demand for wireless radio spectrum bandwidth, better approaches for utilization of the radio spectrum are essential. 1 The automatic modulation classification (AMC) is one of the most common techniques for identifying frequency spectra, spectrum management, electronic warfare, cognitive radio networks, interference

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Modulation recognition based on constellation diagram for M-QAM signals

Cited by 20 publications

References 2 publications

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Modulation Format Identification Based on an Improved RBF Neural Network Trained With Asynchronous Amplitude Histogram

An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

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