Automatic digital modulation recognition using support vector machines and genetic algorithm

Authors:
Jie Li;Jing Peng;Heng Chu;Weile Zhu
Affiliations:
School of Electronic Engineering, University of Electronic Science and Technology of China, Cheng du, Sichuan, China;School of Network Technology, Hebei University of Science and Technology, Shijiazhuang, Hebei, China;School of Electronic Engineering, University of Electronic Science and Technology of China, Cheng du, Sichuan, China;School of Electronic Engineering, University of Electronic Science and Technology of China, Cheng du, Sichuan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

A new method, based on support vector machines (SVMs) and genetic algorithm (GA), is proposed for automatic digital modulation recognition (ADMR). In particular, the best feature subset from the combined statistical feature set and spectral feature set is optimized using genetic algorithm. Compared to the conventional artificial neural network (ANN) method, the method proposed avoids overfitting and local optimal problems. Simulation results show that this method is more robust and effective than other existing approaches, particularly at a low signal noise ratio (SNR).