一种基于多代理强化学习算法的认知物联网资源分配方法
Rong Wang1, Yanjin Shen1, Dongtao Wang2
1Hunan Automotive Engineering Vocational University, Zhuzhou, 412000, China.
Scientific reports
|February 7, 2026
概括
这项研究优化了对车辆中的认知物联网 (CIoT) 的资源配置,以尽量减少信息时代 (AoI). 改进的多代理近接政策优化 (IMAPPO) 算法大大降低了连接车辆的数据延迟.
科学领域:
- 无线通信无线通信
- 物联网的物联网,就是物联网.
- 人工智能的人工智能
背景情况:
- 车辆间通信面临来自动态频谱接入和通道条件的挑战.
- 及时性对于高速车辆网络至关重要,使信息时代 (AoI) 成为一个关键的性能指标.
研究的目的:
- 在车辆的认知物联网 (CIoT) 网络中尽量减少信息时代 (AoI).
- 解决联合道选择和功率控制问题,以在高速移动下分配资源.
主要方法:
- 将问题建模为具有自定义奖励函数的马尔科夫决策过程 (MDP).
- 使用车辆作为代理的多代理强化学习方法.
- 为混合动力行动空间提出了改进的多代理近接政策优化 (IMAPPO) 算法,并增强了Actor网络.
主要成果:
- 拟议的IMAPPO算法有效地管理动态车辆环境中的资源分配.
- 模拟证实了算法的可行性和有效性,减少了系统AOI.
- 在降低车辆用户的AOI方面,CIoT资源分配方案显著超过了替代方法.
结论:
- IMAPPO算法为CIoT车辆网络中的资源分配提供了有效的解决方案.
- 优化AoI对于联网和自动驾驶汽车的实时应用至关重要.
- 这项研究促进了未来汽车通信系统的智能资源管理.
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