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

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.

Automated summary

This paper proposes a modulation recognition algorithm based on feature selection. By using the hyperplane of the support vector machine and the weight vector of features, cumulative features are selected and the modulation type employed at the transmitter is identified. Simulation results show that the proposed algorithm can optimize feature selection for modulation recognition and improve identification efficiency when compared with existing feature selection algorithms.