Modulation Classification of MQAM Signals Using Particle Swarm Optimization and Subtractive Clustering

摘要：

This paper proposes a novel algorithm for modulation recognition of MQAM signals which uses the combination of subtractive clustering (SC) and particle swarm optimization (PSO) (PSO-SC) to extract the discriminating features. The method uses PSO to search for the best clustering radius of SC in order to enable reconstructed constellation optimal. Then, the best clustering radius (CR) is used as classification feature. Compared with the classification methods available using subtractive clustering, the algorithm proposed by this paper has higher correct classification rate in modulation classification for MQAM signals. In addition, simulation results show that the modulation classification method performs robust in the low signal-noise ratio.

关键词： Modulation Classification Constellation Subtractive Clustering Particle Swarm Optimization

作者: Li Yan-ling Li Bing-bing Yin Chang-yi

作者单位: School of Information and Management Science, Henan Agricultural University, Zhengzhou, China Nation National Key Laboratory of ISN, Xidian University, Xi’an, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1537-1540

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）