Research Article
Modulation Recognition Technology of Communication Signals Based on Density Clustering and Sample Reconstruction
@INPROCEEDINGS{10.1007/978-3-030-19086-6_53, author={Hui Han and Xianglong Zhou and Xiang Chen and Ruowu Wu and Yun Lin}, title={Modulation Recognition Technology of Communication Signals Based on Density Clustering and Sample Reconstruction}, proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings}, proceedings_a={ADHIP}, year={2019}, month={5}, keywords={Modulation recognition Entropy features Density clustering Restructure SVM}, doi={10.1007/978-3-030-19086-6_53} }- Hui Han
Xianglong Zhou
Xiang Chen
Ruowu Wu
Yun Lin
Year: 2019
Modulation Recognition Technology of Communication Signals Based on Density Clustering and Sample Reconstruction
ADHIP
Springer
DOI: 10.1007/978-3-030-19086-6_53
Abstract
Modulation recognition is an important part in the field of communication signal processing. In recent years, with the development of modulation recognition technology, various problems have emerged. In this title, we propose an improved recognition framework based on SVM, which extracts the entropy feature of the signal and distinguishes it from the traditional modulation recognition framework. We combine the training set with the test set first, then carry on the density clustering to the whole data set. The data set after the cluster is extracted according to a certain proportion to build a new training set, and the new training set is used to train the SVM. Finally, the data of the test set is modulated by the modulation recognition. Experimental results show that the proposed method improves the recognition rate of traditional SVM framework and enhances the stability of traditional SVM framework.