基于深度强化学习的V2V通信光束管理优化
1Huazhong University of Science and Technology, Wuhan, 430074, China.
Scientific reports
|November 22, 2023
概括
本研究介绍了一种深度强化学习 (DRL) 方法,用于5G NR FR2车辆对车辆 (V2V) 通信中的智能光束管理. DRL方法优化了光束对齐和跟踪,在关键性能指标上表现优于现有方法.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 智能运输系统 智能运输系统
背景情况:
- 智能互联汽车对于智能城市和智能运输至关重要.
- 车辆网络传输各种数据 (安全,传感,多媒体),需要高光谱效率,低延迟和可靠性.
- 5G NR FR2 (24-71 GHz) 推用于V2X,但面临诸如高路径损失和频道波动等挑战.
研究的目的:
- 提出基于深度强化学习 (DRL) 的智能光束管理方法,用于车辆对车辆 (V2V) 通信.
- 解决5G NR FR2 V2X通信中频谱效率,延迟和可靠性之间的权衡问题.
- 在复杂,动态的车辆环境中优化光束对齐和跟踪.
主要方法:
- 为智能光束管理开发了一个深度强化学习 (DRL) 算法.
- DRL方法的重点是对光束对齐和跟踪的最佳控制.
- 进行了模拟,将拟议的方法与5G标准和扩展卡尔曼波器 (EKF) 方法进行比较.
主要成果:
- 与5G标准相比,DRL辅助方法显著减少了通信延迟.
- 拟议的方法在基于EKF的方法上显示出更高的可靠性和光谱效率.
- 在具有挑战性的5G NR FR2 V2X场景中,DRL有效地管理了光束对齐和跟踪.
结论:
- 深度强化学习为5G NR FR2 V2X通信中的智能光束管理提供了有效的解决方案.
- 拟议的DRL方法成功地平衡了光谱效率,延迟和可靠性.
- 这种方法可以提高车辆网络在复杂和动态环境中的性能.
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