LSTM Network Based Traffic Flow Prediction for Cellular Networks

Shulin Cao; Wei Liu

Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8–10, 2019, Proceedings

Research Article

LSTM Network Based Traffic Flow Prediction for Cellular Networks

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@INPROCEEDINGS{10.1007/978-3-030-32216-8_63,
    author={Shulin Cao and Wei Liu},
    title={LSTM Network Based Traffic Flow Prediction for Cellular Networks},
    proceedings={Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8--10, 2019, Proceedings},
    proceedings_a={SIMUTOOLS},
    year={2019},
    month={10},
    keywords={Deep learning Long short-term memory (LSTM) Traffic flow prediction Cellular network},
    doi={10.1007/978-3-030-32216-8_63}
}

Shulin Cao
Wei Liu
Year: 2019
LSTM Network Based Traffic Flow Prediction for Cellular Networks
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-32216-8_63

Shulin Cao¹^,*, Wei Liu¹^,*

1: Xidian University

*Contact email: slcao_cn@stu.xidian.edu.cn, liuweixd@mail.xidian.edu.cn

Abstract

The traffic flow prediction of cellular network requires low complexity and high accuracy, which is difficult to meet using the existing methods. In this paper, we propose an long short-term memory (LSTM) network based traffic flow prediction in which we consider temporal correlations inherently and nonlinear characteristics of cellular network traffic flow data. We use Back Propagation Through Time (BPTT) to train the LSTM network and evaluate the model using mean square error (MSE) and mean absolute error (MAE). Simulation results show that the proposed LSTM network based traffic flow prediction for cellular network is superior to the stacked autoencoder network based algorithm.

Keywords: Deep learning Long short-term memory (LSTM) Traffic flow prediction Cellular network

Published: 2019-10-24
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-32216-8_63