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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基于分解重构和度量学习的深度无意调制特征提取框架

本文主要研究了有意调制（IM）信息对无意调制（UM）特征的强干扰的避免。为了解决这一具有挑战性的问题，提出了一种新的深度UM特征提取框架。将分解重构和度量学习的思想引入深度学习。同时，设计了一个目标函数来自动学习对IM信息不敏感的深度UM特征。实验结果表明，在不同IM参数的测量数据中，深度UM特征具有显著的稳定性和可分离性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.