Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications
The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant sign...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/3/837 |
| _version_ | 1797318290607964160 |
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| author | Jihun Lee Hun Kim Jaewoo So |
| author_facet | Jihun Lee Hun Kim Jaewoo So |
| author_sort | Jihun Lee |
| collection | DOAJ |
| description | The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, we need to find the optimal parameters for directional beamforming, i.e., the antenna beamwidth and beam alignment interval, that maximize the throughput, taking the beam alignment overhead into consideration. In this paper, we propose a reinforcement learning (RL)-based beamforming scheme in a vehicle-to-infrastructure system, where we jointly determine the antenna beamwidth and the beam alignment interval, taking into account the past and future rewards. The simulation results show that the proposed RL-based joint beamforming scheme outperforms conventional beamforming schemes in terms of the average throughput and the average link stability ratio. |
| first_indexed | 2024-03-08T03:49:18Z |
| format | Article |
| id | doaj.art-ed0d77a5e62c43a091c6dd7f78e4cec7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:49:18Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ed0d77a5e62c43a091c6dd7f78e4cec72024-02-09T15:21:59ZengMDPI AGSensors1424-82202024-01-0124383710.3390/s24030837Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I CommunicationsJihun Lee0Hun Kim1Jaewoo So2Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaDepartment of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaDepartment of Electronic Engineering, Sogang University, Seoul 04107, Republic of KoreaThe directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, we need to find the optimal parameters for directional beamforming, i.e., the antenna beamwidth and beam alignment interval, that maximize the throughput, taking the beam alignment overhead into consideration. In this paper, we propose a reinforcement learning (RL)-based beamforming scheme in a vehicle-to-infrastructure system, where we jointly determine the antenna beamwidth and the beam alignment interval, taking into account the past and future rewards. The simulation results show that the proposed RL-based joint beamforming scheme outperforms conventional beamforming schemes in terms of the average throughput and the average link stability ratio.https://www.mdpi.com/1424-8220/24/3/837vehicle communicationsantenna beamwidthbeam alignment overheadbeam alignment intervalreinforcement learning |
| spellingShingle | Jihun Lee Hun Kim Jaewoo So Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications Sensors vehicle communications antenna beamwidth beam alignment overhead beam alignment interval reinforcement learning |
| title | Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications |
| title_full | Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications |
| title_fullStr | Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications |
| title_full_unstemmed | Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications |
| title_short | Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications |
| title_sort | reinforcement learning based joint beamwidth and beam alignment interval optimization in v2i communications |
| topic | vehicle communications antenna beamwidth beam alignment overhead beam alignment interval reinforcement learning |
| url | https://www.mdpi.com/1424-8220/24/3/837 |
| work_keys_str_mv | AT jihunlee reinforcementlearningbasedjointbeamwidthandbeamalignmentintervaloptimizationinv2icommunications AT hunkim reinforcementlearningbasedjointbeamwidthandbeamalignmentintervaloptimizationinv2icommunications AT jaewooso reinforcementlearningbasedjointbeamwidthandbeamalignmentintervaloptimizationinv2icommunications |