Achieving Efficient Feature Representation for Modulation Signal: A Cooperative Contrast Learning Approach-Reference-Cited by-同舟云学术

Achieving Efficient Feature Representation for Modulation Signal: A Cooperative Contrast Learning Approach

Published:2024-05-01 Issue:9 Volume:11 Page:16196-16211
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Bai Jing¹^ORCID,Wang Xu¹,Xiao Zhu²^ORCID,Zhou Huaji³^ORCID,Ali Talal Ahmed Ali²^ORCID,Li You⁴,Jiao Licheng¹^ORCID

Affiliation:

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

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

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

4. AI Group, Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen), Shenzhen, China

Funder

National Natural Science Foundation of China

Key Research and Development Program of Shaanxi Province

Aeronautical Science Foundation of China

Shenzhen Science and Technology Program

Basic and Applied Basic Research Foundation of Guangdong Province

Key Program of National Natural Science Foundation of China

National Key Laboratory of Electromagnetic Space Security

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10508620/10382665.pdf?arnumber=10382665

Reference59 articles.

1. Survey on Service Placement, Provisioning, and Composition for Fog-Based IoT Systems

2. Assisted-Fog-Based Framework for IoT-Based Healthcare Data Preservation

3. Gradient Boosting for Health IoT Federated Learning

4. Physical-Layer Security of 5G Wireless Networks for IoT: Challenges and Opportunities

5. Automatic Modulation Classification via Meta-Learning

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