Automatic Composite-Modulation Classification Using Ultra Lightweight Deep-Learning Network Based on Cyclic-Paw-Print-Reference-Cited by-同舟云学术

Automatic Composite-Modulation Classification Using Ultra Lightweight Deep-Learning Network Based on Cyclic-Paw-Print

Published:2024-06 Issue:3 Volume:10 Page:866-879
ISSN:2332-7731
Container-title:IEEE Transactions on Cognitive Communications and Networking
language:
Short-container-title:IEEE Trans. Cogn. Commun. Netw.

Author:

Yan Xiao¹^ORCID,Yang Pengfei¹^ORCID,Zhong Xunuo¹^ORCID,Wang Qian¹^ORCID,Wu Hsiao-Chun²^ORCID,He Ling¹

Affiliation:

1. School of Aeronautics and Astronautics and the Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

2. School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA, USA

Funder

Natural Science Foundation of Sichuan Province

Louisiana Board of Regents Research Competitiveness Subprogram

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6687307/10551670/10413504.pdf?arnumber=10413504

Reference52 articles.

1. Guest Editorial Special Issue on Collaborative Intelligence for Green Internet of Things in the 6G Era

2. 6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities

3. QoS-Aware Bandwidth Allocation and Concurrent Scheduling for Terahertz Wireless Backhaul Networks

4. Toward RIS-Enhanced Integrated Terrestrial/Non-Terrestrial Connectivity in 6G

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