Journal ArticleDOI
Automatic Modulation Classification Based on Constellation Density Using Deep Learning
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TLDR
In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM to demonstrate better performance compared to many existing classifiers in the literature.Abstract:
Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal’s constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.read more
Citations
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Journal ArticleDOI
A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing
TL;DR: A comprehensive survey of the state-of-the-art DL-based modulation classification algorithms, especially the techniques of signal representation and data preprocessing utilized in these algorithms, is provided in this paper .
Journal ArticleDOI
Multitask-Learning-Based Deep Neural Network for Automatic Modulation Classification
TL;DR: In this paper , a multitask learning-based deep neural network (MLDNN) is proposed, which effectively fuses I/Q and A/P. In addition, the MLDNN also has a novel backbone, which is made up of three blocks to extract discriminative features.
Proceedings ArticleDOI
Learning Constellation Map with Deep CNN for Accurate Modulation Recognition
TL;DR: In this paper, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image, where several grouped and asymmetric CNN layers in each block are organized by a flow-in-flow structure for feature enrichment.
Journal ArticleDOI
A Data Preprocessing Method for Automatic Modulation Classification Based on CNN
TL;DR: In this paper, a novel data preprocessing method is proposed to improve CNN-based automatic modulation classification, which achieved a maximum accuracy of 93.7% when the signal-to-noise ratio is 14 dB.
Journal ArticleDOI
Automatic Modulation Classification Based on Deep Learning for Software-Defined Radio
TL;DR: An improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio, based on the Inception-ResNet network.
References
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
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Survey of automatic modulation classification techniques: classical approaches and new trends
TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
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.
Journal ArticleDOI
Automatic Modulation Classification Using Combination of Genetic Programming and KNN
TL;DR: This paper explores the use of Genetic Programming in combination with K-nearest neighbor (KNN) for AMC and demonstrates that the proposed method provides better classification performance compared to other recent methods.