dc.contributor.author | Manic, Setinder | |
dc.contributor.author | Foh, Chuan Heng | |
dc.contributor.author | Kose, Abdulkadir | |
dc.contributor.author | Lee, Haeyoung | |
dc.contributor.author | Leow, Chee Yen | |
dc.contributor.author | Moessner, Klaus | |
dc.contributor.author | Suthaputchakun, Chakkaphong | |
dc.date.accessioned | 2024-04-03T08:30:00Z | |
dc.date.available | 2024-04-03T08:30:00Z | |
dc.date.issued | 2024-02-12 | |
dc.identifier.citation | Manic , S , Foh , C H , Kose , A , Lee , H , Leow , C Y , Moessner , K & Suthaputchakun , C 2024 , Machine Learning based Beamwidth Adaptation for mmWave Vehicular Communications . in 2023 IEEE 16th Malaysia International Conference on Communication (MICC) : Smart Digital Communication for Humanity . International Conference on Communication (MICC) , Institute of Electrical and Electronics Engineers (IEEE) , Kuala Lumpur, Malaysia , pp. 1-6 , 16th IEEE Malaysia International Conference on Communications 2023 (MICC 2023) , Lumpur , Malaysia , 10/12/23 . https://doi.org/10.1109/MICC59384.2023.10419542 | |
dc.identifier.citation | conference | |
dc.identifier.isbn | 979-8-3503-0434-3 | |
dc.identifier.issn | 2694-5282 | |
dc.identifier.other | ORCID: /0000-0002-5760-6623/work/157084320 | |
dc.identifier.uri | http://hdl.handle.net/2299/27701 | |
dc.description | © 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. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/MICC59384.2023.10419542 | |
dc.description.abstract | The incorporation of mmWave technology in vehicular networks has unlocked a realm of possibilities, propelling the advancement of autonomous vehicles,enhancing interconnectedness, and facilitating communication for intelligent transportation systems (ITS). Despite these strides in connectivity, challenges such as high path-loss have arisen, impacting existing beam management procedures. This work aims to address this issue by improving beam management techniques, specifically focusing on enhancing the service time between vehicles and base stations through adaptive mmWave beamwidth adjustments, accomplished using a Contextual Multi-Armed Bandit Algorithm. By leveraging various conditions to train the ML agent of the Contextual Multi-Armed Bandit Algorithm, it seeks to learn about vehicle mobility profiles and optimize the usage of differentantenna beamwidth settings to maximize seamless connection time. The extensive simulation results showcase the effectiveness of an adaptive beamwidth for mobility profiles, extending the connection time a vehicle experiences with a base station when compared to the existing strategies. | en |
dc.format.extent | 6 | |
dc.format.extent | 274410 | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.ispartof | 2023 IEEE 16th Malaysia International Conference on Communication (MICC) | |
dc.relation.ispartofseries | International Conference on Communication (MICC) | |
dc.title | Machine Learning based Beamwidth Adaptation for mmWave Vehicular Communications | en |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Department of Engineering and Technology | |
dc.contributor.institution | Centre for Engineering Research | |
dc.contributor.institution | Communications and Intelligent Systems | |
rioxxterms.versionofrecord | 10.1109/MICC59384.2023.10419542 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |