Contrastive Self-Supervised Clustering for Specific Emitter Identification-Reference-Cited by-同舟云学术

Contrastive Self-Supervised Clustering for Specific Emitter Identification

Published:2023-12-01 Issue:23 Volume:10 Page:20803-20818
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Hao Xiaoyang¹^ORCID,Feng Zhixi¹^ORCID,Liu Ruoyu¹,Yang Shuyuan¹^ORCID,Jiao Licheng¹^ORCID,Luo Rong²^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. Unit, 91054, People’s Liberation Army of China, Beijing, China

Funder

National Natural Science Foundation of China

Science and Technology Innovation Team in Shaanxi Province of China

Foundation of Intelligent Decision and Cognitive Innovation Center of State Administration of Science, Technology and Industry for National Defense, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10323344/10147285.pdf?arnumber=10147285

Reference60 articles.

1. Specific Emitter Identification via Hilbert–Huang Transform in Single-Hop and Relaying Scenarios

2. RF Fingerprint-Identification-Based Reliable Resource Allocation in an Internet of Battle Things

3. A Novel Method Based on Order Statistics for Extracting Fingerprint of Narrow Band Emitter

4. Automatic Modulation Classification via Meta-Learning

5. Improved wireless security for GMSK-based devices using RF fingerprinting

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