In this paper, we propose a workflow and a deep learning algorithm for recognizing Quadrature amplitude modulation signal(QAM), this design adopts a convolutional neural network (CNN) and Extreme Learning Machine (ELM...
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ISBN:
(数字)9781510639690
ISBN:
(纸本)9781510639690
In this paper, we propose a workflow and a deep learning algorithm for recognizing Quadrature amplitude modulation signal(QAM), this design adopts a convolutional neural network (CNN) and Extreme Learning Machine (ELM) as the core,leverage the powerful feature extraction of CNN and fast classification learning of ELM. The spectrogram image features of the signal obtained by short-time Fourier transform (STFT) are input to the CNN-ELM hybrid model, the modulation mode of the QAM signal is finally recognized by ELM. This algorithm surmounts the shortcomings of traditional methods well, Simulation results also verify the superiority of the proposed system whose classification accuracy is beyond 99.86%.
In this paper, several typical convolutional neural networks, VGG16, VGG19, Inception, Xception and Resnet50, are used to identify the modulationpattern of the constellation. It is found through experiments that the ...
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ISBN:
(纸本)9781728151021
In this paper, several typical convolutional neural networks, VGG16, VGG19, Inception, Xception and Resnet50, are used to identify the modulationpattern of the constellation. It is found through experiments that the Resnet50 network performs best in the recognition of constellations. Then, using the Resnet50 convolutional neural network, the two modes of the modulation mode and the signal-to-noise ratio of the constellation are simultaneously identified. When the accuracy of the signal-to-noise ratio is required to be 1 db, the identified quasi-group rate is 60 percent. When the accuracy of the signal to noise ratio is required to be 2db. The accuracy of recognition can reach 85 percent.
In this paper, the modulation pattern recognition of underwater acoustic communication signals is presented. The classification is evaluated. The experimental results show that the classification accuracy of the prese...
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This paper presents a band adaptive modulation pattern recognition method to against the recognition instability in the electronic war which the signal bandwidth is varying. The method is based on researching decision...
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ISBN:
(纸本)9781424438822
This paper presents a band adaptive modulation pattern recognition method to against the recognition instability in the electronic war which the signal bandwidth is varying. The method is based on researching decision theory and characteristic parameter. The core of the method is that it can restructure the threshold of parameters with changing signal bandwidth and improve the identification results effectively. The simulation results show that: within signal bandwidth ranging from 1 to 7M, the four modulationpatterns can be recognized, recognition rate is more than 95%.
To solve the problem of reconnaissance and processing of broad-band satellite communication signals, a kind of satellite communication signals BPSK/QPSK modulation pattern recognition method was put forward in this pa...
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ISBN:
(纸本)9789811365041;9789811365034
To solve the problem of reconnaissance and processing of broad-band satellite communication signals, a kind of satellite communication signals BPSK/QPSK modulation pattern recognition method was put forward in this paper. This method deals with the satellite descending signal with BPSK/QPSK modulation in the under-sampling condition. Because the corrected spectrum of BPSK signal contains obvious crest, while QPSK signal does not contain this feature. The difference of the waveform characteristics is used to complete modulation pattern recognition. The simulation results show that this method can identify BPSK/QPSK modulation signals when SNR is greater than 1 dB. When the sampling points are reduced, the satellite communication signal under-sampling modulation pattern recognition method can still maintain good recognition performance.
In this paper, in order to settle the problem of unintentional interference between communication devices and obtain effective information quickly and accurately in cognitive radio (CR), and an intelligent modulation ...
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In this paper, in order to settle the problem of unintentional interference between communication devices and obtain effective information quickly and accurately in cognitive radio (CR), and an intelligent modulation pattern recognition method based on wavelet approximate coefficient entropy (WACE) is proposed. Based on the traditional wavelet entropy, an improved wavelet entropy, WACE, is presented, which can characterize the modulated signal pattern and suppress the noise effectively. Furthermore, in order to solve the problem of high complexity for linear weighting calculation, the deep neural network (DNN) is adopted, and the vector of the WACE is used as the input of the DNN to realize intelligent recognition of a variety of typical communication signal modulationpatterns. Simulation results verify the correctness of the theoretical analysis, and show that the proposed intelligent recognition method can effectively realize the modulation pattern recognition of multiple signals at low signal-to-noise ratio (SNR), with relative low computational complexity.
With the development of communication technology, the modulation methods of wireless signals show a diversified trend. modulation pattern recognition is a very key technology in non-cooperative communication systems s...
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ISBN:
(纸本)9798400708688
With the development of communication technology, the modulation methods of wireless signals show a diversified trend. modulation pattern recognition is a very key technology in non-cooperative communication systems such as wireless signal spectrum resource regulation and modern military warfare. When performing wireless signal modulation pattern recognition, the type and quantity of the data set have an important impact on the recognition result, so it is also very important to select or construct a data set reasonably. This article mainly studies the wireless signal dataset and modulation pattern recognition technology. Firstly, provide an overview of wireless signal datasets and introduce the types and construction of wireless signal datasets. Subsequently, the principle of modulation pattern recognition is introduced and the research status of three types of modulationrecognition methods, namely, likelihood ratio recognition method based on decision theory, modulation pattern recognition technique based on feature extraction and modulationrecognition technique based on deep learning, is elaborated. And compare the performance of various modulationrecognition technologies. Finally, a summary and outlook were made on future research directions.
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