Proceedings ArticleDOI
Modulation Classification of VHF Communication System based on CNN and Cyclic Spectrum Graphs
TLDR
A modulation classification method for very high frequency (VHF) signals, which is based on deep convolutional neural network (CNN) and cyclic spectrum graphs is proposed, which has high modulation classification accuracy and less computation burden in low SNR.Abstract:
Modulation classification is the technological basis of adaptive interference mitigation in communication system. This paper proposes a modulation classification method for very high frequency (VHF) signals, which is based on deep convolutional neural network (CNN) and cyclic spectrum graphs. First, the cyclic spectrum of VHF signals is analyzed. Then, a deep learning method based on CNN is proposed, down-sampling and clipping technologies are used for preprocessing cyclic spectrum images, parameters of the proposed neural network are optimized, and finally the modulation classification is realized. The experimental results show that, the proposed method has high modulation classification accuracy and less computation burden in low SNR.read more
Citations
More filters
Journal ArticleDOI
Deep Learning Based Low Complexity Symbol Detection and Modulation Classification Detector
Proceedings ArticleDOI
An Intelligent Maritime Communication Signal Recognition Algorithm based on ACWGAN
TL;DR: This paper studies and analyzes the individual identification technology of the VHF signal based on the rf fingerprint technology of signal and uses the improved adversarial generation network ACWGAN (Auxiliary Classifier Wasserstein Generative Adversarial Networks) to train and identify to obtain a better classification result.
References
More filters
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Book ChapterDOI
Convolutional Radio Modulation Recognition Networks
TL;DR: It is shown that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.
Journal ArticleDOI
Maximum-likelihood classification for digital amplitude-phase modulations
Wen Wei,Jerry M. Mendel +1 more
TL;DR: The study of asymptotic performance shows that the ML classifier is capable of classifying any finite set of distinctive constellations with zero error rate when the number of available data symbols goes to infinity.
Journal ArticleDOI
Modulation Classification Based on Signal Constellation Diagrams and Deep Learning
Shengliang Peng,Hanyu Jiang,Huaxia Wang,Hathal Alwageed,Yu Zhou,Marjan Mazrouei Sebdani,Yu-Dong Yao +6 more
TL;DR: This paper develops several methods to represent modulated signals in data formats with gridlike topologies for the CNN and demonstrates the significant performance advantage and application feasibility of the DL-based approach for modulation classification.
Related Papers (5)
Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input.
A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum
Guangliang Pan,Jun Li,Fei Lin +2 more
Modulation classification based on cyclic spectral features and neural network
Lanjun Qian,Canyan Zhu +1 more