An Approach of Modulation Recognition Using 4th Order Cyclic Cumulants Vector of Digital Signal for Cognitive Radio

Abstract

Modulation recognition is an important issue in cognitive radio research area, however, high recognition precision is usually achieved by relative large number of training data and more various features of digital signal, which call for much more resource. In this paper, a novel modulation recognition approach is proposed, 4th order cyclic cumulants vectors of digital signal is applied for modulation recognition, which are constructed as features to train support vector machine classifiers for further recognition. The experimental result shows the proposed approach can get comparative high precision for ASK, BPSK and PSK signal recognition in additive white Gaussian noise channel while using relative small number of training samples and small in dimension, which reduce cost remarkably.