基于图表注意网络和深度强化学习的C-V2X通信资源的动态分配
Zhijuan Li1,2,3, Guohong Li1, Zhuofei Wu4
1School of Computer and Big Data, Heilongjiang University, Harbin 150080, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一个新的AI框架,以优化车辆通信的资源配置. 在智能交通系统中,GAT-A2C模型提高了交通安全 (V2V) 和数据服务 (V2N).
科学领域:
- 无线通信
- 人工智能
- 智能运输系统
背景情况:
- 车辆对车辆 (V2V) 和车辆对网络 (V2N) 通信对于智能运输系统 (ITS) 是至关重要的.
- 在动态交通环境中,共享V2V (安全) 和V2N (信息娱乐) 频谱资源带来了重大资源分配挑战.
- 现有的方法难以平衡可靠的V2V传输与高速率的V2N服务.
研究的目的:
- 提出一个新的强化学习 (RL) 框架,用于V2V和V2N通信的联合资源分配.
- 应对资源有限和动态的车辆网络环境的挑战.
- 优化资源块和传输功率以提高通信性能.
主要方法:
- 开发了一个图表注意力网络 (GAT) - 优势行为者-批评 (GAT-A2C) 强化学习框架.
- 构建了一个代表V2V链接和干扰关系的图,V2V链接是节点,干扰是边缘.
- 使用GAT捕获干扰模式并将它们与RL环境状态的链路特征结合起来.
- 使用RL代理来共同优化V2V和V2N的资源块分配和传输功率.
主要成果:
- GAT-A2C框架显著提高了V2N数据速率.
- 拟议的方法大大提高了各种车辆密度的V2V通信成功率.
- 在V2N率和V2V沟通成功率上都取得了显著的改善.
结论:
- 在智能车辆网络中,GAT-A2C方法为资源分配提供了有前途的解决方案.
- 该框架具有强大的可扩展性,适用于未来的大规模,动态的交通场景.
- 通过先进的RL技术可以实现V2V和V2N资源的有效联合优化.
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