在车辆物联网网络中的优先意识频谱管理的轻量级增强学习
Adeel Iqbal1, Ali Nauman1, Tahir Khurshaid2
1School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了用于车辆物联网 (V-IoT) 频谱管理的轻量增强学习. 像VPADQ-C和Q-UCB这样的拟议方法显著提高了能源效率,减少了延迟,并提高了智能运输系统的可靠性.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 人工智能的人工智能
背景情况:
- 车载物联网 (V-IoT) 对智能运输系统 (ITS) 至关重要,它支持具有严格服务质量 (QoS) 要求的多种应用程序.
- 现有的频谱管理技术与V-IoT网络的动态性质以及诸如深度强化学习 (DRL) 等先进解决方案的计算需求作斗争.
- 需要有效的实时频谱管理框架,适合在V-IoT中部署路边单元 (RSU).
研究的目的:
- 为V-IoT网络提出基于强化学习 (RL) 的轻量级和可解释的频谱管理框架.
- 引入和评估两个增强的Q-Learning变体:VPADQ-C和Q-UCB,以改善频谱分配.
- 提供一个基线 (风险意识启发式) 来比较基于学习的方法与传统方法.
主要方法:
- 开发了使用受约束的马尔科夫决策过程 (CMDP) 和在线初级-二元优化的价值优先行动双重Q学习与约束 (VPADQ-C).
- 引入了上下文Q-学习与上置信界限 (Q-UCB),包括不确定性意识探索和成功率先验 (SRP).
- 创建了一个全面的模拟框架,模拟交通,色和能量动态,以评估性能指标.
主要成果:
- VPADQ-C显示出优越的能效 (≈8.425×10^7比特/J) 和超过60%的中断概率降低.
- Q-UCB实现了更快的收 (≈190次),最低的阻塞概率 (≈0.0135),以及最小的平均延迟 (≈0.351 ms).
- 这两种方法都超过了传统的Q-Learning和Double Q-Learning,保持了公平性 (≈0.364) 和吞吐量 (≈28 Mbps),具有可扩展的训练时间.
结论:
- 拟议的基于RL的框架为V-IoT中的实时频谱管理提供了可行的解决方案.
- 在能源效率,可靠性,融合速度和延迟方面,VPADQ-C和Q-UCB提供了明显的优势.
- 这些框架适合大规模的V-IoT部署,在密集的车辆流量下满足URLLC级要求.
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