Modulation scheme recognition using convolutional neural network

Zhang, Qianwen; Xu, Zhan; Zhang, Peiyue

doi:10.1049/joe.2018.9188

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…Due to the advancements in the field of image recognition, image processing techniques are being extended to AMR domain. For this purpose, some kind of transformation algorithms have to be employed in order to adapt the techniques previously used for imaging to AMR [68], [72], [74]. However, this kind of data manipulation requires additional time which can complicate the AMR problem in time-sensitive applications.…”

Section: ) Ae-based Methodsmentioning

confidence: 99%

“…Therefore, many researchers in the field of wireless communication are very keen on the idea of converting the signal recognition problem into image recognition one. For example, in [68], a CNN-based AMR was presented in which various signal spectrograms were converted into an image dataset using Short-Time Fourier Transform (STFT). The authors introduced two image classification approaches in order to examine the AMR accuracy.…”

Section: C: Classification Using Image Representationsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

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The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

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Section: ) Ae-based Methodsmentioning

confidence: 99%

Section: C: Classification Using Image Representationsmentioning

confidence: 99%

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

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“…For example, in [27] and [28], using TFD array as input of CNN to recognition different modulations. Similarly, the modulation identification could be regarded as an image recognition problem in which a deep learning network can be trained using TFD images of radar signal [29]- [34]. Then, the test data would be predicted using the trained deep model.…”

Section: B Deep Learning-based Methodsmentioning

confidence: 99%

Automatic Modulation Recognition of Radar Signals Based on Manhattan Distance-Based Features

Huang

Jin

et al. 2019

IEEE Access

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Feature-based (FB) algorithms for automatic modulation recognition of radar signals have received much attention since they are usually simple to realize. However, existing FB approaches usually focus on several specific modulations and fail when applied to various modulations. To overcome this issue, we propose a simple and effective FB algorithm based on Manhattan distance-based features (MDBFs) in this paper. MDBFs are new features for radar signals that can be applied for recognition of different modulations. The main contributions of this paper are as follows. First, radar signals are represented as wavelet ridges, which includes important information that can distinguish different modulations, and the piecewise aggregate approximation algorithm is introduced to reduce signal dimensions. Then, the dynamic time warping averaging is employed instead of the traditional k-means algorithm to extract realistic centroids for each class. Finally, the Manhattan distances between each data sample and each centroid are used to construct MDBFs, and decisions are made using the k-nearest neighbor. In addition, we prove that MDBFs have better class separability power than the Euclidean-based features. MDBFs contain information about the correlations between different classes, which means that these features suitable for discriminating various modulations when their class distributions do not overlap badly in representation space. The extensive experiments on a synthetic dataset demonstrate the outstanding performance of our proposed method and are hardly affected by the pulse width of the signal. Thus, the proposed method with the effectiveness and robustness could be a promising modulation recognition method of the radar signal. INDEX TERMS Modulation recognition, Manhattan distance-based feature, wavelet ridge, dynamic time warping averaging, class centroid.

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“…[16] proposed an attention cooperative framework to improve the classification accuracy and [17] exploited the graph convolutional network. Moreover, other works transformed the radio signals into images, i.e., constellation diagram [18], spectrogram [19], and classified the modulation categories using existing image classifiers. Furthermore, different data augmentation methods are studied [20] [21] to better train deep learning-based classifiers.…”

Section: Related Work a Deep Learning-based Radio Modulation Classifi...mentioning

confidence: 99%

Visualizing Deep Learning-based Radio Modulation Classifier

Huang

You

Pan

et al. 2020

Preprint

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Deep learning has recently been successfully applied in automatic modulation classification by extracting and classifying radio features in an end-to-end way. However, deep learningbased radio modulation classifiers are lack of interpretability, and there is little explanation or visibility into what kinds of radio features are extracted and chosen for classification. In this paper, we visualize different deep learning-based radio modulation classifiers by introducing a class activation vector. Specifically, both convolutional neural networks (CNN) based classifier and long short-term memory (LSTM) based classifier are separately studied, and their extracted radio features are visualized. Extensive numerical results show both the CNN-based classifier and LSTM-based classifier extract similar radio features relating to modulation reference points. In particular, for the LSTM-based classifier, its obtained radio features are similar to the knowledge of human experts. Our numerical results indicate the radio features extracted by deep learning-based classifiers greatly depend on the contents carried by radio signals, and a short radio sample may lead to misclassification.

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Modulation scheme recognition using convolutional neural network

Cited by 10 publications

References 14 publications

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Automatic Modulation Recognition of Radar Signals Based on Manhattan Distance-Based Features

Visualizing Deep Learning-based Radio Modulation Classifier

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