Automatic Modulation Classification in Real Tx/Rx Environment using Machine Learning and SDR

Furtado, Rafael S.; Torres, Yasmim P.; Silva, Mateus O.; Colares, Georgio S.; Pereira, António; Amoedo, Diego A.; Valadao, Myke D.M.; Carvalho, Celso B.; Costa, André L. A.; Waldir, S. S.

doi:10.1109/icce50685.2021.9427693

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Features generally include instantaneous features, statistical features, spectral correlation features and transform domain features, etc. The traditional classifier adopts Decision Trees (DT) [10], K-Neareat Neighbor (KNN) [11], Support Vector Machine (SVM) [12], and Artificial Neural Network (ANN) [13]. Muller et al used the SVM classifier for recognition based on instantaneous and phase characteristics, effectively alleviating the over-dependence of ANN classifier on training samples [14,15].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram

Han

Zhu

et al. 2023

Electronics

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In signal communication based on a non-cooperative communication system, the receiver is an unlicensed third-party communication terminal, and the modulation parameters of the transmitter signal cannot be predicted in advance. After the RF signal passes through the RF band-pass filter, low noise amplifier, and image rejection filter, the intermediate frequency signal is obtained by down-conversion, and then the IQ signal is obtained in the baseband by using the intermediate frequency band-pass filter and down-conversion. In this process, noise and signal frequency offset are inevitably introduced. As the basis of subsequent analysis and interpretation, modulation recognition has important research value in this environment. The introduction of deep learning also brings new feature mining tools. Based on this, this paper proposes a signal modulation recognition method based on multi-feature fusion and constructs a deep learning network with a double-branch structure to extract the features of IQ signal and multi-channel constellation, respectively. It is found that through the complementary characteristics of different forms of signals, a more complete signal feature representation can be constructed. At the same time, it can better alleviate the influence of noise and frequency offset on recognition performance, and effectively improve the classification accuracy of modulation recognition.

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Section: Introductionmentioning

confidence: 99%

Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram

Han

Zhu

et al. 2023

Electronics

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“…The method based on expert features is to transform the received signal into a certain feature space through specific analysis and processing and then design a classifier for classification. Typical expert features include cyclic spectrum feature, high-order Cumulant feature, and wavelet transform feature, while common classifiers include support vector machine [7], decision tree [8], artificial neural network [9], etc. The recognition accuracy of this type of method depends on the extracted statistical features and is limited by the weak learning ability of traditional classifiers.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Lightweight Model for Automatic Modulation Classification: A Hybrid Feature Extraction Network Combined with Attention Mechanism

Ma,

Fang,

Fan

et al. 2023

Electronics

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This paper proposes a hybrid feature extraction convolutional neural network combined with a channel attention mechanism (HFECNET-CA) for automatic modulation recognition (AMR). Firstly, we designed a hybrid feature extraction backbone network. Three different forms of convolution kernels were used to extract features from the original I/Q sequence on three branches, respectively, learn the spatiotemporal features of the original signal from different “perspectives” through the convolution kernels with different shapes, and perform channel fusion on the output feature maps of the three branches to obtain a multi-domain mixed feature map. Then, the deep features of the signal are extracted by connecting multiple convolution layers in the time domain. Secondly, a plug-and-play channel attention module is constructed, which can be embedded into any feature extraction layer to give higher weight to the more valuable channels in the output feature map to achieve the purpose of feature correction for the output feature map. The experimental results on the RadiomL2016.10A dataset show that the designed HFECNET-CA has higher recognition accuracy and fewer trainable parameters compared to other networks. Under 20 SNRs, the average recognition accuracy reached 63.92%, and the highest recognition accuracy reached 93.64%.

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Research direction towards exploring software control radio using machine learning algorithms for smart hotel applications with future aspects

Rajasekhar,

Monisha

2024

Australian Journal of Electrical and Electronics Engineering

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Automatic Modulation Classification in Real Tx/Rx Environment using Machine Learning and SDR

Cited by 7 publications

References 12 publications

Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram

Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram

An Efficient and Lightweight Model for Automatic Modulation Classification: A Hybrid Feature Extraction Network Combined with Attention Mechanism

Research direction towards exploring software control radio using machine learning algorithms for smart hotel applications with future aspects

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