对于IRS辅助的毫米波V2I通信的上下文感知光束选择.
Ricardo Suarez Del Valle1, Abdulkadir Kose2, Haeyoung Lee3
1Department of Electronic Engineering, University of Surrey, Guildford GU2 7XH, UK.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究介绍了一个上下文多武器盗 (C-MAB) 算法,以改善毫米波 (mmWave) 车辆到一切 (V2X) 通信. 这种新的方法通过使用智能反射表面 (IRS) 提高了链接稳定性和覆盖范围.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
背景情况:
- 毫米波 (mmWave) 技术为车辆到一切 (V2X) 通信提供了高带宽和低延迟.
- 毫米波V2X的挑战包括高传播损失和在城市环境中的有限覆盖率.
- 智能反射表面 (IRS) 可以通过改进信号路径来减轻这些问题.
研究的目的:
- 引入一种新的C-MAB (Contextual Multi-Armed Bandit) 算法,用于毫米波V2X的动态光束和IRS选择.
- 在充满挑战的车辆环境中增强链路稳定性,覆盖范围和资源利用.
主要方法:
- 开发一个上下文多武器强盗 (C-MAB) 算法.
- 基于实时环境背景的光束和IRS选择的动态调整.
- 模拟mmWave V2X场景,有或没有IRS的协助.
主要成果:
- 该C-MAB算法显著提高了链接稳定性.
- 与基于SNR和标准MAB策略相比,平均光束逗留时间翻了一番.
- 在IRS的帮助下,高达四倍的业绩增长可以实现.
结论:
- 拟议的C-MAB方法优化了对毫米波V2X通信的资源配置.
- 这种方法大大提高了覆盖范围,数据速率和资源利用率.
- C-MAB为可靠的毫米波V2X连接提供了一个有希望的解决方案.
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