Next Article in Journal
Criticality Analysis of the Lower Ionosphere Perturbations Prior to the 2016 Kumamoto (Japan) Earthquakes as Based on VLF Electromagnetic Wave Propagation Data Observed at Multiple Stations
Next Article in Special Issue
Information Geometry for Covariance Estimation in Heterogeneous Clutter with Total Bregman Divergence
Previous Article in Journal / Special Issue
Low Probability of Intercept-Based Radar Waveform Design for Spectral Coexistence of Distributed Multiple-Radar and Wireless Communication Systems in Clutter
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Entropy 2018, 20(3), 198;

Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
Department of Electrical and Computer Engineering, Western New England University, Springfield, MA 01119, USA
Author to whom correspondence should be addressed.
Received: 30 January 2018 / Revised: 13 March 2018 / Accepted: 14 March 2018 / Published: 16 March 2018
(This article belongs to the Special Issue Radar and Information Theory)
PDF [1772 KB, uploaded 3 May 2018]


In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Rényi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is −10 dB. View Full-Text
Keywords: entropy feature; feature selection; ensemble learning; radar entropy feature; feature selection; ensemble learning; radar

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zhang, Z.; Li, Y.; Jin, S.; Zhang, Z.; Wang, H.; Qi, L.; Zhou, R. Modulation Signal Recognition Based on Information Entropy and Ensemble Learning. Entropy 2018, 20, 198.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top