多头深度Q学习用于连续光束成型,在V2X高速公路通信中选择性MC-CDMA操作
Nguyen Huu Trung1, Nguyen Thuy Anh2, Fuqiang Liu3
1School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 10000, Vietnam. trung.nguyenhuu@hust.edu.vn.
Scientific reports
|August 14, 2025
概括
本研究介绍了动态车辆到一切 (V2X) 通信的综合框架,提高了可靠性和可扩展性. 它使用先进的调制和深度增强学习来实现强大的,适应性的高速车辆链接.
科学领域:
- 无线通信系统无线通信系统
- 网络工程 网络工程
- 人工智能在通信中的应用
背景情况:
- 动态车辆到一切 (V2X) 网络需要强大的通信,以实现高速车辆连接.
- 现有的直角频率分割多重复合 (OFDM) 系统在密集的V2X部署中面临着可靠性和可扩展性的挑战.
- 快速变化的通道条件和移动效应需要适应性的沟通策略.
研究的目的:
- 为大规模动态V2X通信网络提出一个综合框架.
- 在复杂的传播环境中增强信号的稳定性,可靠性和可扩展性.
- 通过使用先进的调制和人工智能,在快速变化的条件下提高适应性性能.
主要方法:
- 实现基于资源块的多载波代码分割多访问 (MC-CDMA) 调制方案.
- 开发一个定制的代码映射器和资源元素 (RE) 分配器,用于干扰感知传输.
- 应用深度强化学习 (DRL) 模型,特别是以物理为灵感的深度Q学习 (DQL) 策略,具有基于强力臂的机制,用于联合光束跟踪和通道条件适应.
主要成果:
- 与OFDM相比,MC-CDMA方案证明了扩展范围的覆盖范围以及卓越的可靠性和可扩展性.
- 基于DRL的光束跟踪有效地纠正了由移动性和多普勒效应引起的误差.
- 在比特错误率 (BER),比特率稳定性,交付流性和光谱效率方面观察到显著的改进.
- 这种带有大型天线阵列的系统确保了连续的光束跟踪,优于传统的RL技术.
结论:
- 拟议的综合框架为未来的支持6G的V2X部署提供了可扩展和适应的解决方案.
- 基于MC-CDMA和DRL的光束跟踪的组合提供了强大的链接质量,这对于高速车辆通信至关重要.
- 该系统的性能凸显了其应对先进V2X通信网络苛刻要求的潜力.
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