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Open Access

Research on throughput prediction of 5G network based on LSTM

Purple Mountain Labs, Nanjing 210000, China
China Communications Construction Second Harbor Engineering Company Ltd., Wuhan 430040, China
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Abstract

This paper proposes a wireless network traffic prediction model based on long-term and short-term memory cyclic neural networks. Through simulation experiments, the throughput prediction of 5G wireless networks using different scheduling algorithms for many different types of services is studied. The results verify that the long short-term memory prediction model has acceptable prediction accuracy and algorithm training speed, meets the needs of wireless network traffic prediction, and has a good application prospect.

Keywords

long short-term memory (LSTM)wireless networkscheduleflow forecastthroughput

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Intelligent and Converged Networks
Volume 3 Issue 2,
June 2022
Pages 217-227
DOI: 10.23919/ICN.2022.0006
Cite this article:
Li L, Ye T. Research on throughput prediction of 5G network based on LSTM. Intelligent and Converged Networks, 2022, 3(2): 217-227. https://doi.org/10.23919/ICN.2022.0006

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Received: 17 February 2022
Revised: 03 March 2022
Accepted: 21 March 2022
Published: 06 September 2022
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license: https://creativecommons.org/licenses/by-nc-nd/3.0/igo/

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