Machine learning based automatic modulation recognition for wireless communications: A comprehensive survey
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 …
with the large increase in the number of end-users have made the radio spectrum more …
Deep learning methods for solving linear inverse problems: Research directions and paradigms
The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …
Innumerable attempts have been carried out to solve different variants of the linear inverse …
[PDF][PDF] 基于信噪比分类网络的调制信号分类识别算法
郭业才， 姚文强 - 电子与信息学报, 2022 - jeit.ac.cn
针对传统降噪算法损伤高信噪比(SNR) 信号而造成信号识别准确率下降的问题,
该文提出基于卷积神经网络的信噪比分类算法, 该算法利用卷积神经网络对信号进行特征提取 …
该文提出基于卷积神经网络的信噪比分类算法, 该算法利用卷积神经网络对信号进行特征提取 …
High-order convolutional attention networks for automatic modulation classification in communication
Automatic modulation classification is a challenging and critical task in the field of
communication. Deep convolutional networks (ConvNets) have been recently applied in …
communication. Deep convolutional networks (ConvNets) have been recently applied in …
Modulation signal classification algorithm based on denoising residual convolutional neural network
Y Guo, X Wang - IEEE Access, 2022 - ieeexplore.ieee.org
Traditional denoising algorithms are easy to lose signal details, resulting in low recognition
accuracy of modulated signals. A modulation signal classification algorithm based on …
accuracy of modulated signals. A modulation signal classification algorithm based on …
Knowledge Embedding Networks based on Deep Learning for Automatic Modulation Classification in Cognitive Radio
Deep learning has shown remarkable success in cognitive radio. However, popular
approaches mainly focus on the purely data-driven architecture design, and fail to explore …
approaches mainly focus on the purely data-driven architecture design, and fail to explore …
Dual-determination of modulation types and signal-to-noise ratios using 2D-ASIQH features for next generation of wireless communication systems
TA Almohamad, MFM Salleh, MN Mahmud… - IEEE …, 2021 - ieeexplore.ieee.org
In order to pursue rapid development of the new generation of wireless communication
systems and elevate their security and efficiency, this paper proposes a novel scheme for …
systems and elevate their security and efficiency, this paper proposes a novel scheme for …
Classification of spectrally efficient constant envelope modulations based on radial basis function network and deep learning
MH Shah, X Dang - IEEE Communications Letters, 2019 - ieeexplore.ieee.org
Despite its significance, modulation classification of constant envelope modulations (CEM)
has not gained worthy attention in AMC literature so far. Two neural network-based …
has not gained worthy attention in AMC literature so far. Two neural network-based …
Modulation signal classification and recognition algorithm based on signal to noise ratio classification network
Y GUO, W YAO - 电子与信息学报, 2022 - jeit.ac.cn
Considering the problem that the traditional noise reduction algorithm damages the high
Signal-to-Noise Ratio (SNR) signal and reduces the accuracy of signal recognition, a SNR …
Signal-to-Noise Ratio (SNR) signal and reduces the accuracy of signal recognition, a SNR …
The performance evaluation of big data-driven modulation classification in complex environment
Z Cai, J Wang, M Ma - IEEE Access, 2021 - ieeexplore.ieee.org
With the proliferation of frequency-using devices and the advent of the era of big data,
spectrum management and control are faced with challenges of effectiveness and accuracy …
spectrum management and control are faced with challenges of effectiveness and accuracy …