Back to Search
Start Over
Automatic modulation classification using different neural network and PCA combinations
- Source :
- Expert Systems with Applications. 178:114931
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- This paper highlights one of the most promising research directions for automatic modulation recognition algorithms, although it does not provide a final solution. We study the design of a high-precision classifier for recognizing PSK, QAM and DVB-S2 APSK modulation signals. First, an efficient pattern recognition model that includes three main modules for feature extraction, feature optimization and classification is presented. The feature extraction module extracts the most useful combinations of up to six high-order cumulants that embed sixth-order moments and uses logarithmic function properties to improve the distribution curve of the six-order cumulants. To the best of our knowledge, this is the first time that these combinations and the improved feature criteria have been applied in this area. The optimizer module selects optimal features via principal component analysis (PCA). Then, in the classifier module, we study two important supervised neural network classifiers (i.e., multilayer perceptron (MLP)- and radial basis function (RBF)-based classifiers). Through an experiment, we determine the best classifier for recognizing the considered modulations. Then, we propose an RBF-PCA combined recognition system in which an optimization module is added to enhance the overall classifier performance. This module optimizes the classifier performance by searching for the best subset of features to use as the classifier input. The simulation results illustrate that the RBF-PCA classifier combination achieves high recognition accuracy even at a low signal-to-noise ratio (SNR) and with limited training samples.
- Subjects :
- 0209 industrial biotechnology
Artificial neural network
business.industry
Computer science
Feature extraction
General Engineering
Pattern recognition
02 engineering and technology
Computer Science Applications
ComputingMethodologies_PATTERNRECOGNITION
020901 industrial engineering & automation
Artificial Intelligence
Multilayer perceptron
Principal component analysis
Pattern recognition (psychology)
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
020201 artificial intelligence & image processing
Radial basis function
Artificial intelligence
business
Classifier (UML)
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 178
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........ef2b2babb30fd201def68babfcb62057