Training Images Generation for CNN Based Automatic Modulation Classification

Zhang, Weitao; Cui, Dan; Lou, Shun-Tian

doi:10.1109/access.2021.3073845

Cited by 17 publications

(8 citation statements)

References 21 publications

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“…In the context of big data, deep learning has always been a research hotspot, in which the powerful feature extraction ability of convolutional neural networks is widely favored [23,24]. The prototype of the convolutional neural network was proposed by the Japanese scientist Fukushima Kunihiko in 1980.…”

Section: The Structure Of the Feature Extractormentioning

confidence: 99%

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Zhang¹,

Gao²,

Shi³

2022

Meas. Sci. Technol.

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Bearing fault diagnosis is a critical component of the mechanical equipment monitoring system. In the complex and harsh environment in which bearings operate, the fault diagnosis approach of multi-source information fusion can extract fault features more stably and extensively than the traditional single-source fault diagnosis method. However, most existing multi-source fusion methods are in infancy, and there are a number of pressing issues to address, such as subjective elements having a significant impact, excessive data redundancy, fuzzy multi-source signal fusion strategy, and insufficient accuracy. As a result, a new multi-source fusion fault diagnosis method is proposed in this paper. First, the residual pyramid algorithm is utilized to fuse the acoustic and vibration signals of multiple spatial positions respectively and then form two fused acoustic and vibration signals. Second, two improved 2D-CNN are used to extract the fault features contained in the above two signals separately to form a multi-source fault feature set. Third, an AdaBoost algorithm with a dynamic deletion mechanism is designed to fuse multi-source fault feature sets and produce the fault diagnosis findings. Finally, six different experimental data sets are used to test the performance of the model. The results reveal that the model has better generalization, higher and more stable fault diagnostic accuracy, and stronger anti-interference capacity.

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Section: The Structure Of the Feature Extractormentioning

confidence: 99%

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Zhang¹,

Gao²,

Shi³

2022

Meas. Sci. Technol.

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“…With the development of CNNs as good image processing and classification tools, several authors in the field of AMC have directed their work towards the utilization of CNNs in the AMC task. Zhang et al 11 studied the utilization of CNNs in this task. They discussed in detail how to generate constellation diagrams from the received signals and use them for training and testing tasks.…”

Section: Related Workmentioning

confidence: 99%

Automatic modulation classification with 2D transforms and convolutional neural network

Ghanem

Shoaib

El‐Gazar

et al. 2022

Trans Emerging Tel Tech

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This article focuses on automatic modulation classification (AMC) in wireless communication systems. A convolutional neural network (CNN) with three layers is introduced for the AMC process. Over degraded channels, it is assumed that the constellation diagrams of received signals do not show sharp points as in the case of pure signals. Instead, the points spread to constitute circle‐shaped objects. With more deterioration in channel conditions, these circle‐shaped objects begin to show overlapping. This behavior motivates us to use object detection, when dealing with the modulation classification task. The selection of the adopted transforms in this article is made from the object detection perspective. Different 2D transforms are considered on the constellation diagrams and compared for better classification performance. These transforms are the Radon transform (RT), the curvelet transform, and the phase congruency (PC). They are applied on the 2D constellation diagrams prior to the classification task with the CNN. The classification of the modulation format at different signal‐to‐noise ratios (SNRs) is considered in this article from the constellation diagrams, and the preprocessed constellation diagrams using RT, curvelet transform, and PC. Seven types of modulation formats are considered in this study to represent both spread and dense constellation diagram patterns, and the study extends from −10 to 10 dB. Analysis of the results indicating the most suitable preprocessor according to the constellation type and the SNR involved is provided.

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“…Recent studies show that deep learning models such as neural networks can extract features effectively from various representation of wireless signals such as in-phase and quadrature (IQ) signal or spectrogram in order to achieve high modulation classification accuracy [3], [4], [5], [6] .…”

Section: Introductionmentioning

confidence: 99%

“…The received signals is preprocessed from I/Q signals cartesian coordinates to polar coordinates to the corresponding amplitude and phase in order to extract more features. By learning more features from the polar domain makes the network more resilient to fading channels [3], [4], [5], [6]. are converted to Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

Elkhatib,

Kamalov,

Moussa

et al. 2024

IEEE Access

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We present an automatic signal modulation classification model using combinatorial deep learning technique. Our proposed deep learning model increase accuracy for low Signal-to-Noise Ratio (SNR) and maintain a high classification accuracy for high SNR signals. Using a hybrid deep learning model combining both ConvLSTM with Transformer-block neural networks, the proposed modulation classifier architecture can learn the signal for both low and high SNR and get better accuracy for signals with high noise. The proposed deep learning modulation classification technique achieves improved classification accuracy of 66% for low SNR signals and 93.5% at high SNR showing that our model is robust under noisy signal modulation. Thus, getting better accuracy in lower SNR signals without sacrifice accuracy for higher SNR signals. An adaptive weighted focal loss function is proposed as an optimized loss function for efficient classification which can be used to control the outliers within a class imbalance and avoid underflow issues. Our deep learning radio modulation classification model works using raw signal without the need of denoising the noisy signal. INDEX TERMS Automatic modulation classification, dynamic spectrum allocation, deep learning techniques, transformer-block convLSTM, feature-based extraction, AI-based wireless communications.

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Training Images Generation for CNN Based Automatic Modulation Classification

Cited by 17 publications

References 21 publications

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Bearing fault diagnosis method based on multi-source heterogeneous information fusion

Automatic modulation classification with 2D transforms and convolutional neural network

Radio Modulation Classification Optimization Using Combinatorial Deep Learning Technique

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