Memory-full context-aware predictive mobility management in dual connectivity 5G networks

Mohamed, A., Imran, M. , Xiao, P. and Tafazolli, R. (2018) Memory-full context-aware predictive mobility management in dual connectivity 5G networks. IEEE Access, 6, pp. 9655-9666. (doi: 10.1109/ACCESS.2018.2796579)

154634.pdf - Accepted Version



Network densification with small cell deployment is being considered as one of the dominant themes in the fifth generation (5G) cellular system. Despite the capacity gains, such deployment scenarios raise several challenges from mobility management perspective. The small cell size, which implies a small cell residence time, will increase the handover (HO) rate dramatically. Consequently, the HO latency will become a critical consideration in the 5G era. The latter requires an intelligent, fast and light-weight HO procedure with minimal signalling overhead. In this direction, we propose a memory-full context-aware HO scheme with mobility prediction to achieve the aforementioned objectives. We consider a dual connectivity radio access network architecture with logical separation between control and data planes because it offers relaxed constraints in implementing the predictive approaches. The proposed scheme predicts future HO events along with the expected HO time by combining radio frequency performance to physical proximity along with the user context in terms of speed, direction and HO history. To minimise the processing and the storage requirements whilst improving the prediction performance, a user-specific prediction triggering threshold is proposed. The prediction outcome is utilised to perform advance HO signalling whilst suspending the periodic transmission of measurement reports. Analytical and simulation results show that the proposed scheme provides promising gains over the conventional approach.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad
Authors: Mohamed, A., Imran, M., Xiao, P., and Tafazolli, R.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
ISSN (Online):2169-3536
Published Online:23 January 2018
Copyright Holders:Copyright © 2018 IEEE
First Published:First published in IEEE Access 6:9655-9666
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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