在车辆边缘网络中的多用户计算卸载和资源分配算法
Xiangyan Liu1, Jianhong Zheng1, Meng Zhang2
1School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究介绍了一个深度决定性政策梯度 (DDPG) 算法,以优化车辆边缘计算网络 (VECN) 的计算卸载和资源配置. 拟议的方法提高了服务质量 (QoS),并将系统延迟降低了24%至29%.
科学领域:
- * 车辆边缘计算网络 (VECN)
- * 移动计算 移动计算
- * 网络资源的分配.
背景情况:
- * 车辆在VECN中的移动性引入了通道状态信息的不确定性.
- *这种不确定性挑战了车辆边缘计算服务器 (VECS) 中计算卸载和资源分配的服务质量 (QoS).
研究的目的:
- * 为了应对VECN中不确定的通道状态信息的挑战.
- * 开发一个针对多用户计算卸载和资源分配的优化模型.
- * 尽量减少总系统延迟,并确保任务执行的 QoS.
主要方法:
- * 将问题建模为混合整数非线性编程 (MINLP) 问题.
- * 提出了基于深度决定性政策梯度 (DDPG) 的算法,用于处理大型状态空间和混合离散/连续行动空间.
- *将基于DDPG的方案与三个基准算法进行了比较.
主要成果:
- *基于DDPG的方案有效地选择任务卸载模式并分配VECS资源.
- * 确保了任务执行的 QoS,并证明了稳定性和可扩展性.
- *与现有技术相比,总完成时间减少了24-29%.
结论:
- * 拟议的 DDPG 算法为 VECN 中的计算卸载和资源分配提供了有效的解决方案.
- * 该方法确保了 QoS 并减少了系统延迟,优于基线方法.
- * 在车辆边缘计算场景中显示了显著的效率和性能改进.
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