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, 358, pp. 479-489. (doi: 10.1016/j.neucom.2019.01.031)
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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 |
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| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Imran, Professor Muhammad and Öztürk, 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 358: 479-489 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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