A novel deep learning driven low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)

Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A. and Imran, M. A. (2019) A novel deep learning driven low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA). Neurocomputing, (doi:10.1016/j.neucom.2019.01.031) (In Press)

Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A. and Imran, M. A. (2019) A novel deep learning driven low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA). Neurocomputing, (doi:10.1016/j.neucom.2019.01.031) (In Press)

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Abstract

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper proposes a novel analytical model for a holistic handover (HO) cost evaluation, that integrates signaling overhead, latency, call dropping, and radio resource wastage. The developed mathematical model is applicable to several cellular architectures, but the focus here is on the Control/ Data Separation Architecture (CDSA). Furthermore, HO prediction is proposed and evaluated as part of the holistic cost for the first time, including through the novel application of a recurrent deep learning architecture, specifically, a stacked long-short-term memory (LSTM) model. Simulation results and preliminary analysis reveal different cases where non-predictive and predictive deep neural networks can be utilized, complying with the low cost and effective HO management requirement. Both analytical and machine learning models are evaluated with real-world human behaviors and interactions modeling data set. Numerical and comparative simulation results demonstrate the potential of our proposed framework in designing an enhanced, deep-learning driven HO management.

Item Type:Articles
Status:In Press
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and OZTURK, Metin and Onireti, Oluwakayode
Authors: Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Neurocomputing
Publisher:Elsevier
ISSN:0925-2312
ISSN (Online):0925-2312
Published Online:17 January 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Neurocomputing 2019
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3007250Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)EP/P028764/1ENG - Systems Power & Energy