Leveraging intelligence from network CDR data for interference aware energy consumption minimization

Zoha, A., Saeed, A., Farooq, H., Rizwan, A. and Imran, M. A. (2018) Leveraging intelligence from network CDR data for interference aware energy consumption minimization. IEEE Transactions on Mobile Computing, 17(7), pp. 1569-1582. (doi:10.1109/TMC.2017.2773609)

151365.pdf - Accepted Version



Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better QoS

Item Type:Articles
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Rizwan, Ali
Authors: Zoha, A., Saeed, A., Farooq, H., Rizwan, A., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Mobile Computing
ISSN (Online):1558-0660
Published Online:15 November 2017
Copyright Holders:Copyright © 2017 IEEE
First Published:First published in IEEE Transactions on Mobile Computing 17(7): 1569-1582
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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