scholarly journals Communication Signal Modulation Mechanism Based on Artificial Feature Engineering Deep Neural Network Modulation Identifier

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fei Lu ◽  
Zhenjiang Shi ◽  
Rijian Su
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Deep Neural Network ◽  
Recognition Rate ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signals ◽  
Communication Signal ◽  
Neuron Activation ◽  
The Neural Network

Based on the characteristics of time domain and frequency domain recognition theory, a recognition scheme is designed to complete the modulation identification of communication signals including 16 analog and digital modulations, involving 10 different eigenvalues in total. In the in-class recognition of FSK signal, feature extraction in frequency domain is carried out, and a statistical algorithm of spectral peak number is proposed. This paper presents a method to calculate the rotation degree of constellation image. By calculating the rotation degree and modifying the clustering radius, the recognition rate of QAM signal is improved significantly. Another commonly used method for calculating the rotation of constellations is based on Radon transform. Compared with the proposed algorithm, the proposed algorithm has lower computational complexity and higher accuracy under certain SNR conditions. In the modulation discriminator of the deep neural network, the spectral features and cumulative features are extracted as inputs, the modified linear elements are used as neuron activation functions, and the cross-entropy is used as loss functions. In the modulation recognitor of deep neural network, deep neural network and cyclic neural network are constructed for modulation recognition of communication signals. The neural network automatic modulation recognizer is implemented on CPU and GPU, which verifies the recognition accuracy of communication signal modulation recognizer based on neural network. The experimental results show that the communication signal modulation recognizer based on artificial neural network has good classification accuracy in both the training set and the test set.

scholarly journals Modulation Recognition of Communication Signal Based on Convolutional Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2302
Author(s):  
Kaiyuan Jiang ◽  
Xvan Qin ◽  
Jiawei Zhang ◽  
Aili Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Recognition Task ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signal

In the noncooperation communication scenario, digital signal modulation recognition will help people to identify the communication targets and have better management over them. To solve problems such as high complexity, low accuracy and cumbersome manual extraction of features by traditional machine learning algorithms, a kind of communication signal modulation recognition model based on convolution neural network (CNN) is proposed. In this paper, a convolution neural network combines bidirectional long short-term memory (BiLSTM) with a symmetrical structure to successively extract the frequency domain features and timing features of signals and then assigns importance weights based on the attention mechanism to complete the recognition task. Seven typical digital modulation schemes including 2ASK, 4ASK, 4FSK, BPSK, QPSK, 8PSK and 64QAM are used in the simulation test, and the results show that, compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR, which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

scholarly journals Research on signal modulation based on machine learning intelligent algorithm and computer automatic identification

2021 ◽  
Vol 2083 (4) ◽  
pp. 042092
Author(s):  
Zixi Li
Keyword(s):  
Neural Network ◽  
Signal To Noise Ratio ◽  
Feature Representation ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Modulation Recognition ◽  
Data Set ◽  
Communication Signals ◽  
Communication Signal ◽  
Automatic Modulation Recognition

Abstract In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of the input convolution neural network model, the signal further optimizes the traditional method of manual extraction of expert features for communication signal recognition, which has great limitations and low accuracy under low signal-to-noise ratio, and the simulation results are verified. The results show that the proposed method has stronger feature representation ability and competitiveness in automatic modulation recognition, and is helpful to promote the application of deep learning in the field of automatic modulation recognition.

Signals Modulation Recognition Based on Efficient Attribute Reduction with Neighborhood Rough Set

2012 ◽  
Vol 220-223 ◽  
pp. 2301-2307
Author(s):  
Ying Zheng Han ◽  
Juan Ping Wu ◽  
Xiao Fang Liang
Keyword(s):  
Rough Set ◽  
Attribute Reduction ◽  
Recognition Rate ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signals ◽  
Time Frequency ◽  
Feature Parameters ◽  
Neighborhood Rough Set ◽  
Propagation Network

The purpose of communication signals automatic modulation recognition is to judge signal modulation styles and estimate signal modulation parameters on the precondition of unknown modulation information. According to the seven kinds communication modulation signals studied in this paper, select a group of feature parameters based on the time-frequency characteristics of communication signals. The fast algorithm for attribute reduction based on neighborhood rough set using feature selection is introduced in detail. Then, using back propagation network as classification instruments to identify signals. The simulation shows that the method can not only reduce the number of feature parameters, but also improve the recognition rate.

Analysis and Research of Modulation Recognition Algorithm in Satellite Communications

2013 ◽  
Vol 756-759 ◽  
pp. 1961-1967
Author(s):  
Ren Bin ◽  
Zhu Ping ◽  
Cheng Ying ◽  
Song Lei
Keyword(s):  
Software Defined Radio ◽  
Satellite Communication ◽  
Recognition Rate ◽  
Signal Amplitude ◽  
Satellite Communications ◽  
Recognition Algorithm ◽  
Automatic Identification ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signal

Automatic identification of the communication signal modulation mode is a hot topic of research in the signal processing field in recent years. It is an important part of electronic countermeasures, and also a rapidly developed field of signal analysis. Communication signal modulation recognition is widely used in signal confirmation, interference identification, radio listening, signal monitoring, as well as software-defined radio, satellite communications and other fields. This paper proposed a digital modulation signal automatic recognition algorithm for satellite communication applications,. The algorithm uses the higher order cumulants of modulating signal, combined with constellation map features, to classify the signal. It has invariance in signal amplitude and phase deviation, and inhibits the additive Gaussian noise at the same time. Compared with other identification algorithms, it has the characteristics of stability and wide application. Computer simulations show that, under the given data length and moderate SNR conditions, a higher recognition rate (> 95%) can be obtained.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

2014 ◽  
Vol 608-609 ◽  
pp. 459-467 ◽  
Author(s):  
Xiao Yu Gu
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Decision Theory ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Modulation Recognition ◽  
The Core ◽  
Feature Parameters ◽  
Communication Signal ◽  
Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

scholarly journals Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data

2019 ◽  
Vol 12 (S10) ◽  
Author(s):  
Jie Hao ◽  
Youngsoon Kim ◽  
Tejaswini Mallavarapu ◽  
Jung Hun Oh ◽  
Mingon Kang
Keyword(s):  
Neural Network ◽  
Survival Analysis ◽  
Cancer Patient ◽  
Clinical Data ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Patient Survival ◽  
Biological Mechanisms ◽  
Neural Network Architecture ◽  
The Neural Network

Abstract Background Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. Results We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified. Conclusions Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.

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