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A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks

Authors :
Nasrin Bahra
Samuel Pierre
Source :
Telecom, Vol 2, Iss 2, Pp 199-212 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.

Details

Language :
English
ISSN :
26734001
Volume :
2
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Telecom
Publication Type :
Academic Journal
Accession number :
edsdoj.8fe272a1dd49d5ae318af1f25923dd
Document Type :
article
Full Text :
https://doi.org/10.3390/telecom2020013