在V2I通信中基于强化学习的联合光束宽度和光束对齐间隔优化
Jihun Lee1, Hun Kim1, Jaewoo So1
1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了一种强化学习 (RL) 方法,用于优化5G车辆通信中的光束成形. 通过智能地管理天线光束宽度和光束对齐间隔,RL方案提高了平均吞吐量和链路稳定性.
科学领域:
- 无线通信是一种无线通信.
- 天线理论天线理论
- 机器学习是机器学习.
背景情况:
- 5G车辆通信需要高数据速率,通常使用定向天线和光束成形.
- 波束成形需要精确的波束对齐,导致显著的信号空头.
- 优化光束宽度和对齐间隔对于平衡吞吐量和开销至关重要.
研究的目的:
- 为车辆到基础设施 (V2I) 系统开发一个优化的光束成形策略.
- 为了共同确定最佳的天线光束宽度和光束对齐间隔.
- 为了最大限度地提高系统吞吐量,同时最大限度地减少信号开销.
主要方法:
- 提出了一个基于强化学习 (RL) 的梁成型方案.
- 该RL代理共同优化天线光束宽度和光束对齐间隔.
- 该计划考虑了决策的过去和未来奖励.
主要成果:
- 拟议的基于RL的联合光束成形方案明显优于传统方法.
- 在平均吞吐量方面取得了明显的改善.
- 展示了增强的平均链路稳定率.
结论:
- 基于RL的联合优化有效地解决了V2I通信中吞吐量和信号开销之间的权衡.
- 这种方法为高效的5G车辆通信系统提供了卓越的解决方案.
- 拟议的方法提高了数据传输速率和连接可靠性.
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