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Intelligent and Converged Networks 2022, 3(2): 217-227 https://doi.org/10.23919/ICN.2022.0006
Open Access | Issue | Published: 06 September 2022

Research on throughput prediction of 5G network based on LSTM

Show Author's Information Lanlan Li1( ),  Tao Ye2
Purple Mountain Labs, Nanjing 210000, China
China Communications Construction Second Harbor Engineering Company Ltd., Wuhan 430040, China
Keywords:
long short-term memory (LSTM), wireless network, schedule, flow forecast, throughput
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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Abstract Full text About this article

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.


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About this article

Research on throughput prediction of 5G network based on LSTM

Show Author's information Lanlan Li1( ), Tao Ye2
Purple Mountain Labs, Nanjing 210000, China
China Communications Construction Second Harbor Engineering Company Ltd., Wuhan 430040, China

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 network, schedule, flow forecast, throughput

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Publication history

Received: 17 February 2022
Revised: 03 March 2022
Accepted: 21 March 2022
Published: 06 September 2022
Issue date: June 2022

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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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