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

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2024-10-25 DOI:10.1109/LCOMM.2024.3486280

Wei Zhang;Lutao Liu;Yilin Jiang;Yuxin Liu

引用次数: 0

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.

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.