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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
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