Spatial-Temporal Cellular Traffic Prediction for 5G and Beyond: A Graph Neural Networks-Based Approach-Reference-Cited by-同舟云学术

Spatial-Temporal Cellular Traffic Prediction for 5G and Beyond: A Graph Neural Networks-Based Approach

Published:2023-04 Issue:4 Volume:19 Page:5722-5731
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wang Zi¹^ORCID,Hu Jia²^ORCID,Min Geyong²^ORCID,Zhao Zhiwei³^ORCID,Chang Zheng³^ORCID,Wang Zhe²^ORCID

Affiliation:

1. School of Computer Science and Technology, University of Electronic Science and Technology of China, Sichuan, China

2. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, U.K.

3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Funder

EU Horizon 2020 INITIATE project

Royal Society International Exchanges

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10081096/09801679.pdf?arnumber=9801679

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4. A multi-source dataset of urban life in the city of Milan and the Province of Trentino

5. Communication-efficient learning of deep networks from decentralized data;mcmahan,0

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