Prediction of Cellular Network Channel Utilization Based on Graph Convolutional Networks-Reference-Cited by-同舟云学术

Prediction of Cellular Network Channel Utilization Based on Graph Convolutional Networks

Published:2022-09-12 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
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Author:

Zhu Rui¹,Luo Xingshuang¹,Yao Jiayi¹,Zhu Xinning²,Zhang Chunhong²

Affiliation:

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications,Beijing,China

2. Key Laboratory of Universal Wireless Communications, Ministry of Education,Beijing,China

Funder

Beijing University of Posts and Telecommunications

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9977437/9977449/09978155.pdf?arnumber=9978155

Reference14 articles.

1. Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features

2. Deep Learning on Graphs: A Survey

3. Spatiotemporal Modeling and Prediction in Cellular Networks;wang;A Big Data Enabled International Conference on Computer Communications (INFOCOM),2017

4. Research on City Traffic Flow Forecast Based on Graph Convolutional Neural Network

5. Time series model for forecasting the number of new admission inpatients

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