Abstract
The introduction of the new 4G technologies promises to satisfy the increasing demands of the end-users for bandwidth consuming applications. However, the high data rates provided by 4G networks at the air interface raise the need for more efficient management of the backhaul resources. In the current work, the authors study the problem of the efficient management of the backhaul resources at the side of the base station. Specifically, a novel scheme is proposed that, initially, predicts the forthcoming demand using artificial neural networks and, then, based on the prediction results, it proactively requests the commitment of the appropriate resources using linear optimisation techniques. The experimental results show that the proposed scheme can efficiently and cost-effectively manage the backhaul resources, outperforming the traditional flat commitment approaches.
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The collected data are the aggregated demand experienced by the BS and correspond to a mixture of services requested by the subscribers.
References
Cisco: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018. White paper (2014)
Yi, S., Lei, M.: Backhaul resource allocation in LTE-Advanced relaying systems. In: Wireless Communications and Networking Conference, pp. 1207–1211 (2012)
Ranaweera, C., Wong, E., Lim, C., Nirmalathas, A., Jayasundara, C.: An efficient resource allocation mechanism for LTE-GEPON converged networks. J. Netw. Syst. Manage. 22(3), 437–461 (2014)
Riggio, R., Gomez, K., Goratti, L., Fedrizzi, R., Rasheed, T.: V-Cell: going beyond the cell abstraction in 5G mobile networks. In: IEEE Network Operations and Management Symposium, pp. 1–5 (2014)
NGMN: Optimised Backhaul Requirements. White paper (2008)
ITU-T G.984.3: Gigabit-capable Passive Optical Networks (G-PON): Transmission convergence layer specification. Recommendation (2008)
Zhang, P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Mitchell, T.: Machine Learning. McGraw-Hill, Maidenhead (1997)
Specht, D.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)
Farlow, S.: The GMDH algorithm of ivakhnenko. Am. Stat. 35(4), 210–215 (1981)
NGMN: Guidelines for LTE Backhaul Traffic Estimation. White paper (2011)
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Loumiotis, I., Adamopoulou, E., Demestichas, K., Theologou, M. (2015). Optimal Backhaul Resource Management in Wireless-Optical Converged Networks. In: Giaffreda, R., Cagáňová, D., Li, Y., Riggio, R., Voisard, A. (eds) Internet of Things. IoT Infrastructures. IoT360 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 151. Springer, Cham. https://doi.org/10.1007/978-3-319-19743-2_36
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DOI: https://doi.org/10.1007/978-3-319-19743-2_36
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