Abstract
The increasing proliferation of 4G mobile technologies is expected to satisfy the constantly growing demand for wireless broadband services. However, the high data rates provided by 4G networks at the air interface raise the need for more efficient management of the backhaul resources especially if the backhaul network has been leased by the mobile operator. In the present work, the authors investigate on the backhaul resource allocation problem at the side of the base station (BS) and a novel distributed scheme is proposed that can efficiently forecast the aggregated traffic demand at the BS using artificial neural networks. It is shown that the proposed scheme provides a mean absolute percentage error of about 10 % for the downlink traffic and about 19 % for the uplink traffic.
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Cisco: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018. White Paper (2014)
Cox, C.: An Introduction to LTE, 2nd edn. Wiley-Blackwell, New York (2014)
Orphanoudakis, T., Kosmatos, E., Angelopoulos, J., Stavdas, A.: Exploiting PONs for mobile backhaul. IEEE Commun. Mag. 51(2), S27–S34 (2013)
Prehofer, C., Bettstetter, C.: Self-organization in communication networks: principles and design paradigms. IEEE Commun. Mag. 43(7), 78–85 (2005)
Papagiannaki, K., Taft, N., Zhang, Z.L., Diot, C.: Long-term forecasting of internet backbone traffic. IEEE Trans. Neural Netw. 16(5), 1110–1124 (2005)
Zhu, Y., Zhang, G., Qiu, J.: Network traffic prediction based on particle swarm BP neural network. J. Netw. 8(11), 2685–2691 (2013)
Loumiotis, I., et al.: On the predictability of next generation mobile network traffic using artificial neural networks. Int. J. Commun. Syst. (2013). doi:10.1002/dac.2728
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)
Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Loumiotis, I., Adamopoulou, E., Demestichas, K., Kosmides, P., Theologou, M. (2015). Artificial Neural Networks for Traffic Prediction in 4G Networks. In: Mumtaz, S., Rodriguez, J., Katz, M., Wang, C., Nascimento, A. (eds) Wireless Internet. WICON 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-319-18802-7_20
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DOI: https://doi.org/10.1007/978-3-319-18802-7_20
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