Classification-Driven Discrete Neural Representation Learning for Semantic Communications-Reference-Cited by-同舟云学术

Classification-Driven Discrete Neural Representation Learning for Semantic Communications

Published:2024-05-01 Issue:9 Volume:11 Page:16061-16073
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Hua Wenhui¹^ORCID,Xiong Longhui²,Liu Sicong²^ORCID,Chen Lingyu²^ORCID,Hong Xuemin²^ORCID,Mota João F. C.³^ORCID,Cheng Xiang⁴^ORCID

Affiliation:

1. School of Electronic Science and Engineering and the National-Local Joint Engineering Research Center of Navigation and Location Services, Xiamen University, Xiamen, China

2. School of Informatics and the National-Local Joint Engineering Research Center of Navigation and Location Services, Xiamen University, Xiamen, China

3. School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, U.K.

4. State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing, China

Funder

National Natural Science Foundation of China

Open Research Fund of the National Mobile Communications Research Laboratory at Southeast University

Natural Science Foundation of Fujian Province of China

EPSRC’s New Investigator Award

New Cornerstone Science Foundation through the XPLORER PRIZE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10508620/10411860.pdf?arnumber=10411860

Reference66 articles.

1. A Novel Architecture Design of Power Internet of Things Based on Huawei Internet of Things Platform

2. Internet of Autonomous Vehicles: Architecture, Features, and Socio-Technological Challenges

3. 6G networks: Beyond Shannon towards semantic and goal-oriented communications

4. Deep Learning Enabled Semantic Communication Systems

5. Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks

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