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:Zoha, Dr Ahmed and Rizwan, Ali and Imran, Professor Muhammad
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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