Wang, S. and Zhang, L. orcid.org/0000-0002-4535-3200 (2024) Support Vector Machine-based Handover Scheme for Heterogeneous Ultra Dense Network of High-Speed Railway. IET Communications, 18 (18). pp. 1191-1204. ISSN 1751-8628
Abstract
In order to meet the growing demands and extend network coverage for high-speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). IET Communications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Aug 2024 14:47 |
Last Modified: | 04 Dec 2024 16:00 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1049/cmu2.12814 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:216474 |