Modulation Recognition Technology of Communication Signals Based on Density Clustering and Sample Reconstruction

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.

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