Metin Ozturk
A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)
Ozturk, Metin; Gogate, Mandar; Onireti, Oluwakayode; Adeel, Ahsan; Hussain, Amir; Imran, Muhammad A.
Authors
Dr. Mandar Gogate M.Gogate@napier.ac.uk
Senior Research Fellow
Oluwakayode Onireti
Ahsan Adeel
Prof Amir Hussain A.Hussain@napier.ac.uk
Professor
Muhammad A. Imran
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 reports a number of significant contributions. First, a novel analytical model is proposed for 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). Second, data-driven HO prediction is proposed and evaluated as part of the holistic cost, for the first time, through novel application of a recurrent deep learning architecture, specifically, a stacked long-short-term memory (LSTM) model. Finally, simulation results and preliminary analysis reveal different cases where non-predictive and predictive deep neural networks can be effectively utilized, based on HO management requirements. Both analytical and machine learning models are evaluated with a benchmark, real-world dataset measuring human behaviors and interactions. Numerical and comparative simulation results demonstrate the potential of our proposed deep learning-driven HO management framework, as a future benchmark for the mobile networking and machine learning communities.
Citation
Ozturk, M., Gogate, M., Onireti, O., Adeel, A., Hussain, A., & 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, 479-489. https://doi.org/10.1016/j.neucom.2019.01.031
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 7, 2019 |
Online Publication Date | Jan 17, 2019 |
Publication Date | 2019-09 |
Deposit Date | Apr 26, 2022 |
Publicly Available Date | Apr 26, 2022 |
Journal | Neurocomputing |
Print ISSN | 0925-2312 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 358 |
Pages | 479-489 |
DOI | https://doi.org/10.1016/j.neucom.2019.01.031 |
Keywords | Mobility management; Deep learning; Predictive handover; 5G wireless mobile networks; Control/Data Separation Architecture |
Public URL | http://researchrepository.napier.ac.uk/Output/2867029 |
Files
A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)
(1.5 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
You might also like
Arabic sentiment analysis using dependency-based rules and deep neural networks
(2022)
Journal Article
Detecting Alzheimer’s Disease Using Machine Learning Methods
(2022)
Conference Proceeding
A Generative Learning Approach to Sensor Fusion and Change Detection
(2016)
Journal Article
A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management
(2018)
Book Chapter
Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances
(2020)
Conference Proceeding