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.
The financial support of the program of Key Industry Innovation Chain of Shaanxi Province, China (2017ZDCXL-GY-04-02), of the program of Xi’an Science and Technology Plan (201805029YD7CG13(5)), Shaanxi, China, of National S&T Major Project (No. 2016ZX03001022-003), China, and of Key R&D Program - The Industry Project of Shaanxi (Grant No. 2018GY-017) are gratefully acknowledged.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Cao, S., Liu, W. (2019). LSTM Network Based Traffic Flow Prediction for Cellular Networks. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_63
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DOI: https://doi.org/10.1007/978-3-030-32216-8_63
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