Deep Unintentional Modulation Feature Extraction Framework Based on Decomposition Reconstruction and Metric Learning

Abstract

In this letter, the avoiding of the powerful interference of intentional modulation (IM) information on unintentional modulation (UM) feature is primarily studied. To address this challenging issue, a novel framework for deep UM feature extraction is proposed. The ideas of decomposition reconstruction and metric learning are introduced into deep learning. Meanwhile, an objective function is designed to automatically learn the deep UM feature that is insensitive to the IM information. The experimental results show the remarkable stability and separability of the deep UM feature across measured data with variable IM parameters.