Modulation Signal Recognition Based on Information Entropy and Ensemble Learning

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.

[1]  Faouzi Kossentini,et al.  Generalized S transform , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  -. Qiang,et al.  Graph Processing on GPUs , 2018, ACM Comput. Surv..

[4]  Li Yi-bing.  Modulation recognition using entropy features and SVM , 2012 .

[5]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[6]  V. Koivunen,et al.  Waveform Recognition in Pulse Compression Radar Systems , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.

[7]  Xinwei Zheng,et al.  Radar emitter classification for large data set based on weighted-xgboost , 2017 .

[8]  A. Wayne Whitney,et al.  A Direct Method of Nonparametric Measurement Selection , 1971, IEEE Transactions on Computers.

[9]  Lijuan Liu,et al.  Research on Kernel Function of Support Vector Machine , 2014, EMC/HumanCom.

[10]  Roberto Sassi,et al.  Low Computational Cost for Sample Entropy , 2018, Entropy.

[11]  Roberto Cristi,et al.  Extraction of polyphase radar modulation parameters using a wigner-ville distribution - radon transform , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[13]  Rabah Attia,et al.  Performance of AdaBoost classifier in recognition of superposed modulations for MIMO TWRC with physical-layer network coding , 2017, 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[14]  Nedyalko Petrov,et al.  Identification of radar signals using neural network classifier with low-discrepancy optimisation , 2013, 2013 IEEE Congress on Evolutionary Computation.

[15]  Yongjun Zhao,et al.  Automatic recognition of radar signals based on time-frequency image character , 2013 .

[16]  Olivier J. J. Michel,et al.  Measuring time-Frequency information content using the Rényi entropies , 2001, IEEE Trans. Inf. Theory.

[17]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[18]  Victor C. M. Leung,et al.  Exploiting Adversarial Jamming Signals for Energy Harvesting in Interference Networks , 2017, IEEE Transactions on Wireless Communications.

[19]  J. Richman,et al.  Sample entropy. , 2004, Methods in enzymology.

[20]  Hamid Aghvami,et al.  CRB-RPL: A Receiver-Based Routing Protocol for Communications in Cognitive Radio Enabled Smart Grid , 2017, IEEE Transactions on Vehicular Technology.

[21]  Janusz Mroczka,et al.  Gabor Transform, SPWVD, Gabor-Wigner Transform and Wavelet Transform - Tools For Power Quality Monitoring , 2010 .

[22]  Shuai Liu,et al.  A Novel Distance Metric: Generalized Relative Entropy , 2017, Entropy.

[23]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[24]  Yang Li,et al.  Radar HRRP target recognition based on Gradient Boosting Decision Tree , 2016, 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[25]  Xiaoyu Zhang,et al.  Recognition of radar emitter signals based on SVD and AF main ridge slice , 2015, Journal of Communications and Networks.

[26]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[27]  Qihui Wu,et al.  Cellular-Base-Station-Assisted Device-to-Device Communications in TV White Space , 2015, IEEE Journal on Selected Areas in Communications.

[28]  Yang Song,et al.  IKNN: Informative K-Nearest Neighbor Pattern Classification , 2007, PKDD.

[29]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[30]  Boualem Boashash,et al.  Editorial Time-frequency approach to radar detection, imaging, and classification , 2010 .

[31]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[32]  Lili Guo,et al.  FRACTAL COMPLEXITY-BASED FEATURE EXTRACTION ALGORITHM OF COMMUNICATION SIGNALS , 2017 .

[33]  Chao Wang,et al.  A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks , 2016, Sensors.

[34]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[35]  Zheng Dou,et al.  The individual identification method of wireless device based on dimensionality reduction and machine learning , 2019, The Journal of Supercomputing.

[36]  Tong Liu,et al.  Research on modulation recognition with ensemble learning , 2017, EURASIP J. Wirel. Commun. Netw..

[37]  J. Friedman.  Stochastic gradient boosting , 2002 .

[38]  Yi Liu,et al.  FS_SFS: A novel feature selection method for support vector machines , 2006, Pattern Recognit..

[39]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[40]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[41]  Wei Zuo,et al.  Robust radar waveform recognition algorithm based on random projections and sparse classification , 2014 .

[42]  Wenshan Wang,et al.  A data authentication scheme for UAV ad hoc network communication , 2017, The Journal of Supercomputing.

[43]  Cheng Li,et al.  Radar Emitter Signal Recognition Based on Sample Entropy and Fuzzy Entropy , 2011, IScIDE.

[44]  Stan Sclaroff,et al.  Efficient nearest neighbor classification using a cascade of approximate similarity measures , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[45]  Han Zhuangzhi,et al.  Realization of Radar Warning Receiver Simulation System , 2015 .