Robust Modulation Classification Over $\alpha$-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis

Yan, Xiao; Liu, Guannan; Wu, Hsiao-Chun; Zhang, Guoyu; Wang, Qian; Wu, Yiyan

doi:10.1109/tvt.2020.2965137

Cited by 43 publications

(22 citation statements)

References 37 publications

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“…the SOI in this paper, and the underwater acoustic noise is obtained by combining the AWGN with the non-Gaussian impulsive noise shown in Eqs. (2) and (8), respectively. The performance of the proposed underwater acoustic signal denoising algorithm based on AWMF+GDES is presented and compared with different algorithms obtained by combining AWMF, with Semi-soft, MSSA, MPSO, and ECABC.…”

Section: B Simulated Results Of the Wavelet Threshold Optimization Mmentioning

confidence: 99%

A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

Wang

Zhang

Yan

et al. 2021

IEEE Trans. Veh. Technol.

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Section: B Simulated Results Of the Wavelet Threshold Optimization Mmentioning

confidence: 99%

A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

Wang

Zhang

Yan

et al. 2021

IEEE Trans. Veh. Technol.

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“…However, there are several promising solutions for AMR in the presence of impulsive non-Gaussian noise such as the Fractional Lower-Order Cyclic-Spectrum (FLOCS), the Spatial Sign Cyclic Correlation Function (SSCCF), the Fractional Lower-Order Cyclic Autocorrelation Function (FLOCAF), and the Cyclic Correntropy Function (CCF). The authors in [30] employed the graph-based FLOCS analysis in the α-stable impulsive noise environment for AMR. Firstly, the FLOCS of the received signal was obtained by the transformation of its fractional lower-order moments.…”

Section: A Ml-amr Methods For Siso Systemsmentioning

confidence: 99%

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

et al. 2021

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The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.

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“…There are mainly two methods for modulation recognition in an impulsive noise environment, one is to extract the features of fractional lower order statistics which are also effective in the non-Gaussian environment [9][10][11], and the other is to remove impulsive noise [12]. In this paper, an adaptive weight myriad filter [13] is used, which can effectively remove impulsive noise and extract information with less distortion.…”

Section: Adaptive Weighted Myriad Filtersmentioning

confidence: 99%

“…However, the electromagnetic environment faced by wireless communication is very complex, including various interferences and noises, and many of these signals and noises involved are non-Gaussian. Compared with Gaussian noise, one common feature of these noises and signals is they have significant impulsive characteristics and are often called impulsive noise, which can be simulation by alpha-stable distribution [9]. Recently, some scholars have studied modulation recognition in impulsive noise environment, Câmara et al [10] analyzed the characteristics of the cyclic correntropy function and fractional lower-order cyclic autocorrelation function in impulsive noise environment and designed a robust modulation recognition structure based on these two kinds of cyclic descriptors.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

2021

KSII TIIS

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In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

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Robust Modulation Classification Over $\alpha$-Stable Noise Using Graph-Based Fractional Lower-Order Cyclic Spectrum Analysis

Cited by 43 publications

References 37 publications

A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

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