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基于ML的延迟优化,用于光纤无线接入网络中的移动边缘计算

Antimbala Marmat¹, Dolly Thankachan¹

¹Department of Electronics and Communication Engineering, Oriental University, Indore, MP, India.

|September 24, 2025

概括

本研究介绍了一种机器学习框架,以减少移动边缘计算 (MEC) 网络中的延迟. 它优化了流量管理和任务卸载,大大提高了实时应用程序的性能.

科学领域:

计算机科学计算机科学
电信工程电信工程电信工程
人工智能的人工智能

背景情况:

通过光纤无线网络进行移动边缘计算 (MEC),由于资源限制,在满足超低延迟需求方面面临挑战.
传统的方法,如集中式学习和静态路由是低效的,容易出现拥堵,浪费宝贵的网络资源.

研究的目的:

提出一种基于机器学习的新框架,以优化MEC网络中的延迟.
整合先进的AI范式,以进行主动的流量管理,智能路由和高效的,保护隐私的任务卸载.

主要方法:

该框架使用自主监督学习,联合学习,时空图神经网络 (GNN),自适应多代理增强学习和超图变压器.
这些方法可以实现动态拥堵意识路由,准确的时空交通预测,以及对延迟敏感应用程序的最佳资源切片.
超图形变压器促进了跨网络切片的动态资源分配.

主要成果:

演示了显著的性能改进,包括34-42%的端到端延迟减少和29-35%更快的任务执行.
减少了50%的培训时间,提高了交通预测准确度,并减少了切片切换延迟.
验证了框架在优化资源切片和延迟意识任务卸载方面的有效性.

结论:

拟议的机器学习框架为下一代MEC网络提供了强大的解决方案,这些网络在严格的性能和隐私限制下运行.
它有效地解决了动态拥堵意识路线和主动交通预测方面的挑战.
该框架支持关键的低延迟应用程序,如增强现实和自动驾驶.

关键词:

有光纤无线网络的无线网络.延迟优化优化延迟优化机器学习是机器学习.移动边缘计算移动边缘计算处理过程处理过程强化学习是一种强化学习.

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