Abstract
Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented.
Original language | English |
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Title of host publication | 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring) - Proceedings |
Place of Publication | USA |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Number of pages | 6 |
ISBN (Electronic) | 9798350311143 |
DOIs | |
Publication status | Published - 14 Aug 2023 |
Event | 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring) - Florence, Italy Duration: 20 Jun 2023 → 23 Jun 2023 Conference number: 97 https://ieeexplore.ieee.org/xpl/conhome/10199114/proceeding |
Publication series
Name | IEEE Vehicular Technology Conference |
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Volume | 2023-June |
ISSN (Print) | 1550-2252 |
ISSN (Electronic) | 2577-2465 |
Conference
Conference | 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring) |
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Abbreviated title | VTC2023 |
Country/Territory | Italy |
City | Florence |
Period | 20/06/23 → 23/06/23 |
Internet address |
Keywords
- V2X
- beam allocation
- mmWave networks