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)
|
Text
151365.pdf - Accepted Version 4MB |
Abstract
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 |
---|---|
Status: | Published |
Refereed: | Yes |
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 |
Publisher: | IEEE |
ISSN: | 1536-1233 |
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 |
University Staff: Request a correction | Enlighten Editors: Update this record