Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems  

作　　者：ZHOU Shuai LI Tao LI Yongzhao 

机构地区：[1]School of Telecommunications Engineering,Xidian University,Xi’an 710071,China

出　　处：《Chinese Journal of Electronics》2023年第4期785-792,共8页电子学报（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(62001358,61771365);the Fundamental Research Funds for the Central Universities(JB190115,JBF180101);the National Key R&D Program of China(254);the National Science Foundation for Post-doctoral Scientists of China(2019M663630).

摘　　要：The feature-based(FB)algorithms are widely used in modulation classification due to their low complexity.As a prerequisite step of FB,feature selection can reduce the computational complexity without significant performance loss.In this paper,according to the linear separability of cumulant features,the hyperplane of the support vector machine is used to classify modulation types,and the contribution of different features is ranked through the weight vector.Then,cumulant features are selected using recursive feature elimination(RFE)to identify the modulation type employed at the transmitter.We compare the performance of the proposed algorithm with existing feature selection algorithms and analyze the complexity of all the mentioned algorithms.Simulation results verify that the proposed RFE algorithm can optimize the selection of the features to realize modulation recognition and improve identification efficiency.

关 键 词：Multiple input multiple output Modulation classification Feature selection Support vector machine-recursive feature elimination 

分 类 号：TN911.3[电子电信—通信与信息系统]