scholarly journals Automatic Modulation Recognition Based on Hybrid Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qiang Duan ◽  
Jianhua Fan ◽  
Xianglin Wei ◽  
Chao Wang ◽  
Xiang Jiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
Hybrid Neural Network ◽  
Memory And Attention ◽  
Computation Efficiency ◽  
Automatic Modulation Recognition ◽  
Short Time

Recognizing signals is critical for understanding the increasingly crowded wireless spectrum space in noncooperative communications. Traditional threshold or pattern recognition-based solutions are labor-intensive and error-prone. Therefore, practitioners start to apply deep learning to automatic modulation classification (AMC). However, the recognition accuracy and robustness of recently presented neural network-based proposals are still unsatisfactory, especially when the signal-to-noise ratio (SNR) is low. In this backdrop, this paper presents a hybrid neural network model, called MCBL, which combines convolutional neural network, bidirectional long-short time memory, and attention mechanism to exploit their respective capability to extract the spatial, temporal, and salient features embedded in the signal samples. After formulating the AMC problem, the three modules of our hybrid dynamic neural network are detailed. To evaluate the performance of our proposal, 10 state-of-the-art neural networks (including two latest models) are chosen as benchmarks for the comparison experiments conducted on an open radio frequency (RF) dataset. Results have shown that the recognition accuracy of MCBL can reach 93% which is the highest among the tested DNN models. At the same time, the computation efficiency and robustness of MCBL are better than existing proposals.

scholarly journals Machine Learning for Modulation Classification of Radar Signals: A Survey

10.31645/17 ◽  
2019 ◽  
Author(s):  
Hitham Alshoubaki ◽  
Keyword(s):  
Neural Network ◽  
Modulation Classification ◽  
Computation Complexity ◽  
Modulation Recognition ◽  
Low Snr ◽  
The Neural Network ◽  
Military Applications ◽  
Automatic Modulation Recognition ◽  
Deep Learning Neural Network

Automatic modulation recognition of radar waveform is a major topic and has many military applications. This paper surveys the models and the techniques used in recognizing different modulation types of intercepted radar waveform. The literature shows the outstanding performance of deep learning neural network at low SNR values and in signal- overlapped environments as well. Additionally, using different mathematical and statistical algorithms demonstrated that utilized in features extraction of the data in order to feed them into the neural network improves the performance significantly. However, reducing computation complexity is in development too.

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.

scholarly journals Automatic Modulation Recognition for MFSK Using Modified Covariance Method

2015 ◽  
Vol 5 (3) ◽  
pp. 429
Author(s):  
Hanan M.Hamee ◽  
Jafer Wadi
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Modulation Index ◽  
Modulation Classification ◽  
A Priori Information ◽  
Modulation Recognition ◽  
Digital Signals ◽  
Signal To Noise ◽  
Automatic Modulation Recognition ◽  
Priori Information

This paper presents modulation classification method capable of classifying<br />MFSK digital signals without a priori information using modified covariance<br />method. This method using for calculation features for FSK modulation<br />should have a good properties of sensitive with FSK modulation index and<br />insensitive with signal to noise ratio SNR variation. The numerical<br />simulations and investigation of the performance by the support vectors<br />machine one against all (SVM-OAA) as a classifier for classifying 6 digitally<br />modulated signals which gives probability of correction classification up to<br />85.85 at SNR=-15dB.

scholarly journals Implementation of Deep Learning-based Automatic Modulation Classifier on FPGA SDR Platform

Electronics ◽  
2018 ◽  
Vol 7 (7) ◽  
pp. 122 ◽  
Author(s):  
Zhi-Ling Tang ◽  
Si-Min Li ◽  
Li-Juan Yu
Keyword(s):  
Neural Network ◽  
Radio Spectrum ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
The Neural Network ◽  
Automatic Modulation Recognition ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
On Chip ◽  
Automatic Modulation Classifier

Intelligent radios collect information by sensing signals within the radio spectrum, and the automatic modulation recognition (AMR) of signals is one of their most challenging tasks. Although the result of a modulation classification based on a deep neural network is better, the training of the neural network requires complicated calculations and expensive hardware. Therefore, in this paper, we propose a master–slave AMR architecture using the reconfigurability of field-programmable gate arrays (FPGAs). First, we discuss the method of building AMR, by using a stack convolution autoencoder (CAE), and analyze the principles of training and classification. Then, on the basis of the radiofrequency network-on-chip architecture, the constraint conditions of AMR in FPGA are proposed from the aspects of computing optimization and memory access optimization. The experimental results not only demonstrated that AMR-based CAEs worked correctly, but also showed that AMR based on neural networks could be implemented on FPGAs, with the potential for dynamic spectrum allocation and cognitive radio systems.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

scholarly journals IRLNet: A Short-Time and Robust Architecture for Automatic Modulation Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 143661-143676
Author(s):  
Huogen Yang ◽  
Lingzhu Zhao ◽  
Guangxue Yue ◽  
Bolin Ma ◽  
Wei Li
Keyword(s):  
Modulation Recognition ◽  
Automatic Modulation Recognition ◽  
Short Time

The Algorithm of Modulation Classification Based on Eigenface and Support Vector Machine in Low SNR

2014 ◽  
Vol 701-702 ◽  
pp. 442-448
Author(s):  
Xiang Ke Guo ◽  
Rong Ke Liu ◽  
Chang Yun Liu ◽  
Shao Hua Yue
Keyword(s):  
Support Vector Machine ◽  
Signal To Noise Ratio ◽  
Spectral Feature ◽  
Support Vector ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
Low Snr ◽  
Feature Extract ◽  
Modulation Signal ◽  
Novel Algorithms

To improve the accuracy and reliability of modulation recognition at low signal to noise ratio (SNR) and few knowledge of signal parameter, the novel method based on the cyclic spectral feature and support vector machine(SVM) is presented. In the process of novel algorithms, the cyclic spectral analysis is used to realize the feature extract of the modulated signals, and the Eigenface method is used to reduce the amount of spectral coherence feature. Then, a new scheme of classification based on support vector machine is presented to classify the modulation signal. The experiment shows that the modulation classification accuracy of presented method is significantly improved at low SNR environment.

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