Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks

Li, Hui; Li, Ming

doi:10.1371/journal.pone.0234068

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…whereas, the amount of input layer nodes is denoted as a the amount of output layer nodes is provided as b, the amount of HL nodes is given as n and a constant values among [1][2][3][4][5][6][7][8][9][10] are represented as c:…”

Section: Modulation Signal Classificationmentioning

confidence: 99%

“…Wavelet transforms Higher order cumulant, multi cumulant, and signal spectral related features are few characteristics i.e., presented in the study for supervised classifications. Conversely, few unsupervised clustering based approaches were introduced for AMC [7]. It considers the merits of the constellation diagrams of the obtained modulation candidates and hence it doesn't need the group labels to train the classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

Venugopal¹,

Mohan²,

Ramesh³

et al. 2022

Computer Systems Science and Engineering

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In recent times, pattern recognition of communication modulation signals has gained significant attention in several application areas such as military, civilian field, etc. It becomes essential to design a safe and robust feature extraction (FE) approach to efficiently identify the various signal modulation types in a complex platform. Several works have derived new techniques to extract the feature parameters namely instant features, fractal features, and so on. In addition, machine learning (ML) and deep learning (DL) approaches can be commonly employed for modulation signal classification. In this view, this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks (CSM-FFDNN). The goal of the CSM-FFDNN model is to classify the different types of digitally modulated signals. The proposed CSM-FFDNN model involves two major processes namely FE and classification. The proposed model uses Sevcik Fractal Dimension (SFD) technique to extract the fractal features from the digital modulated signals. Besides, the extracted features are fed into the DNN model for modulation signal classification. To improve the classification performance of the DNN model, a barnacles mating optimizer (BMO) is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised. A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN model. The experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

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Section: Modulation Signal Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

Venugopal¹,

Mohan²,

Ramesh³

et al. 2022

Computer Systems Science and Engineering

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show abstract

“…Because of these characteristics, LTE/LTE-A is a suitable option, offering cost-efficient and quick to be deployed. The LTE/LTE-A deployment architecture utilized in this research is point-to-point (PTP) since it aims to improve video transmission by taking the dynamic nature of broadband wireless networks and adjusting modulation based on the signals received and SNR measurements [ 18 ]. LTE/LTE-A provides a variety of modulations that may get complex when data rates are large.…”

Section: Introductionmentioning

confidence: 99%

Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks

Bin-Salem

Wan

Naeem

et al. 2022

J Wireless Com Network

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Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK $$\rightarrow$$ → 16-QAM) and from 0 to 43.3% for (16-QAM $$\rightarrow$$ → 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK $$\rightarrow$$ → 16-QAM)and (16-QAM $$\rightarrow$$ → 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.

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Design and implementation of improved CNN activation function

Tang

Tian

Liu

et al. 2022

2022 3rd International Conference on Computer Vision, Image and Deep Learning &Amp; International Conference on Computer Engine

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Analysis of the pattern recognition algorithm of broadband satellite modulation signal under deformable convolutional neural networks

Cited by 5 publications

References 32 publications

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks

Design and implementation of improved CNN activation function

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