Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features

Güner, Ahmet; Alçın, Ömer Faruk; Şengür, Abdulkadir

doi:10.1016/j.measurement.2019.05.061

Cited by 48 publications

(18 citation statements)

References 22 publications

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“…The simulation results of the proposed AMC2N are compared with those of existing methods, in particular, CNN [38], R-CNN [39], CL [40], and LBP [41]. The reason behind selecting these methods for comparison is that the contributions of these methods are similar to the contributions, i.e., preprocessing, feature extraction, and classification of the proposed method.…”

Section: Results Analysismentioning

confidence: 99%

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AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

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Section: Results Analysismentioning

confidence: 99%

“…Feature extraction algorithms, such as LBP and DWT [41,42], have high false positive results that lead to a reduction in classification accuracy. They also extract limited features from the given signal that tends to degrade the efficacy of feature extraction.…”

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confidence: 99%

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

et al. 2021

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“…A variety of machine learning techniques have been applied to classify 3D point clouds [9]. In addition, supervised machine learning is utilized by presenting prelabeled examples to obtain useful predictive models that can be applied to new data [10,11]. Especially in the last few years, neural networks have been the basis of the methods used by advanced computer vision algorithms in many areas such as classification [12], segmentation [13] and target detection [14].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Object Classification Methods Using 3D Zernike Moment on 3D Point Clouds

Özbay¹,

Çınar²

2019

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The point clouds provide responsive geometric representation on many applications. The classification of objects through point clouds is one of the popular subjects of recent years. In this study, we introduce the potential of the 3D Zernike Moment approach for the object classification on the 3D point cloud. Zernike Moment (ZM) has utilized as a feature extractor of point clouds. This paper presents a comparative study of the state-of-the-art classification methods with respect to machine learning algorithms and PointNet which have been developed for classification by Stanford University. Object classification has been applied to a dataset with labeled 3D Zernike Moment features inferences obtained from the 3D point cloud. The performance of the developed method is verified by comparing the experimental results on the Washington RGB-D Object Dataset which consists of forty-five different household objects as point cloud data. Fine Gaussian SVM gives the best results in accuracy (96.0%) according to the results obtained with built-in cross-validation results. The results of the proposed classification of 3D Point Clouds on the 3D Zernike Moment features have significantly higher accuracy. The classification of 3D Zernike Moments on point cloud compared to directly point cloud classification is efficient and effective and lower complexity computation is obtained. It is emphasized that the 3D Zernike Moment features can be optimized for classification. In general, the comparative validation results have been reached a high accuracy in the proposed method. In the future, 3D Zernike Moment feature extractions are emphasized for the usability of classification operations on 3D data.

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“…In recent research, there was an interest in the development of advanced machine learning methods to increase that accuracy. In this trend, several models were proposed based on deep learning: deep belief networks [7], extreme learning machines [8], stacked autoencoders [1], or convolutional neural networks [9], [10], [11], [12], [13].…”

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confidence: 99%

“…The state-of-art methods mentioned above have reached high levels of accuracy in classifying the modulation type. In some cases, accuracies higher than 90% were reported [8], [12], [13]. However, there are still some challenges, mainly the need for larger datasets for training with all desired and possible modulation techniques in order to train the neural networks properly.…”

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confidence: 99%

Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification

Pinto-Orellana

Hammer

2020

IEEE Access

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In this paper, we presented a novel spectral estimation method, the dyadic aggregated autoregressive model (DASAR), that characterizes the spectrum dynamics of a modulated signal. DASAR enhances automatic modulation classification (AMC) on environments where new or unknown modulation techniques are introduced, and only size-restricted data is accessible to train classification algorithms. A key component for obtaining efficient machine learning-based classification is the development of valuable knowledge-descriptive features. DASAR constructs a multi-level spectral representation by subdividing a signal into successive dyadic segments where each partition is modeled as an aggregation of singlefrequency autoregressive processes. Thus, the model ensures a robust representation at the segment level, while the multi-level decomposition can capture time-varying spectra. As a feature extraction model, DASAR can provide useful learning features related to signals with complex spectra. The effectiveness of our model was tested on a dataset comprised of 11 different modulation techniques and realistic transmission medium characteristics. Using only 200 128-point samples per modulation scheme (1% of the available signal samples) and a proper selection of a classification algorithm, DASAR reaches accuracy up to 70.96% compared with a maximum accuracy of 43.62% using the state-of-art methods tested under the same conditions. INDEX TERMS Automatic modulation classification, signal processing, spectrum modeling, autoregressive model, machine learning. I. INTRODUCTION A reliable communication system enforces that a transmitted message will only be understood if the transmitter and receiver have complete information about the communication parameters, including the type and characteristics of the desired modulation (i.e., the techniques to adapt the message to the transmission medium conditions). However, it is feasible to infer some properties about the communication channel's configuration if we intercept the modulated message passing through the channel. This inference is the central goal in automatic modulation classification (AMC) [1]. AMC was developed for military applications such as electronic warfare, surveillance, and threat analysis [2]. Nevertheless, its relevance made it fundamental for other widespread civilian purposes, including software-defined radio (SDR) and spectrum management. SDR is the base of selforganized communication infrastructures that rely on AMC to recognize the modulation in real-time and to adapt or optimize resources during the ongoing transmission process. The rapid progress and spread of high-speed wireless communication networks (especially 5G and WiFi 802.11ax), and the consequent spectrum saturation, impulse the role of AMC in spectrum management allowing the regulatory entities to detect misused spectrum ranges by non-authorized communication channels. AMC algorithms can be classified into likelihood-based (LB-AMC) and feature-based (FB-AMC) methods [1], [2],

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Automatic digital modulation classification using extreme learning machine with local binary pattern histogram features

Cited by 48 publications

References 22 publications

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

A Comparative Study of Object Classification Methods Using 3D Zernike Moment on 3D Point Clouds

Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification

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