Skip to main content

Research Repository

Advanced Search

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

Metin Ozturk

Oluwakayode Onireti

Ahsan Adeel

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.

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





You might also like



Downloadable Citations