Wang, S and Zhang, L orcid.org/0000-0002-4535-3200 (2023) Q-learning based Handover Algorithm for High-Speed Rail Wireless Communications. In: 2023 IEEE Wireless Communications and Networking Conference (WCNC). WCNC 2023: IEEE Wireless Communications and Networking Conference, 26-29 Mar 2023, Glasgow, UK. IEEE ISBN 978-1-6654-9123-5
Abstract
High-speed railways (HSRs) has become one of the most preferable modes of transportation. In the evolution of the railway wireless communication system from Long Term Evolution for Railway (LTE-R) to the 5th Generation Wireless System (5G), the rapid increase in the train speed and number of base stations along the railway track led to challenging handover (HO) problems, such as high failure rate and frequent HOs. In order to address this challenge, an improved handover decision strategy is proposed based on Q-learning algorithm. The simulation results demonstrate that our proposed scheme is capable of reducing the number of unnecessary handover and improving the network performance remarkably.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Handover, Mobility, High-speed Railway, Qlearning, 5G |
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: | 16 Mar 2023 15:06 |
Last Modified: | 05 Oct 2023 13:49 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/WCNC55385.2023.10119037 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:197341 |