对于5G V2X网络的深度强化学习辅助光束跟踪和数据传输,5G V2X网络的数据传输
1ANTD, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899 USA.
IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council
|March 7, 2024
概括
本研究介绍了一种强化学习 (RL) 方法,用于5G车辆到一切 (V2X) 网络中的光束跟踪. 该方法提高了车辆与基础设施 (V2I) 通信的跟踪精度和数据速率,即使车辆速度很高.
科学领域:
- 无线通信无线通信
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 对于5G车辆到一切 (V2X) 网络,特别是车辆到基础设施 (V2I) 通信在更高频率 (例如5G FR2) 中,光束跟踪至关重要.
- 挑战包括路径丢失,时间间隔缩短,车辆高速和定位错误,在光束跟踪精度和数据速率之间产生权衡.
- 现有的方法很难在高频V2I通信的严格时间限制下保持性能.
研究的目的:
- 为5G V2X网络开发一种高效的光束跟踪方法,以平衡跟踪精度和数据速率.
- 为应对高频V2I通信的挑战,包括短时间段和车辆动态.
- 在5G FR2通信的限制下,提高光束跟踪的时间效率.
主要方法:
- 提出了一种辅助强化学习 (RL) 的高分辨率代码本式光束跟踪方法.
- 评估并选择双延迟深确定性政策梯度 (TD3) 框架,以确定光束模式的效率.
- 综合循环神经网络 (RNN),由赫斯特指数分析提供信息,以提高RL框架的性能.
主要成果:
- 拟议的RL辅助方法有效地在短时间内确定正确的光束模式.
- 与传统方法相比,在光束跟踪精度方面取得了显著的改进.
- 实现了V2I通信的增强数据速率和卓越的时间效率.
结论:
- 基于TD3的RL框架,增强了RNNs,为5G V2X中的光束跟踪提供了一个强大的解决方案.
- 该方法成功地在高频V2I场景中实现了跟踪精度和数据速率之间的权衡.
- 拟议的方法为优化动态车辆环境中的V2I通信性能提供了一个有希望的方向.
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