Integrating Prior Knowledge and Contrast Feature for Signal Modulation Classification-Reference-Cited by-同舟云学术

Integrating Prior Knowledge and Contrast Feature for Signal Modulation Classification

Published:2024-06-15 Issue:12 Volume:11 Page:21461-21473
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Bai Jing¹^ORCID,Liu Xuebo¹,Wang Yiran¹^ORCID,Xiao Zhu²^ORCID,Chen Feng³,Zhou Huaji⁴^ORCID,Jiao Licheng¹^ORCID

Affiliation:

1. School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China

2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

3. Signal Processing Laboratory, Shanghai Electro-Mechanical Engineering Institute, Shanghai, China

4. National Key Laboratory of Electromagnetic Space Security, Jiaxing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Aeronautical Science Foundation of China

Key Research and Development Program of Shaanxi Province

Key Projects of the Natural Science Foundation

Cultivation Project of Yuelu Mountain Industrial Innovation Center

Shenzhen Science and Technology Program

Basic and Applied Basic Research Foundation of Guangdong Province

National Key Laboratory of Electromagnetic Space Security

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10551641/10473047.pdf?arnumber=10473047

Reference47 articles.

1. GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification

2. Multi-Component Feature Extraction for Few-Sample Automatic Modulation Classification

3. A Survey of Modulation Classification Using Deep Learning: Signal Representation and Data Preprocessing

4. Automatic Modulation Classification via Meta-Learning

5. A Modulation Classification Algorithm for Multipath Signals Based on Cepstrum

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