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工业PON中的无噪声任务卸载:粒子-卡尔曼级联过与纳什平衡驱动的冷却

Mengxin Zhang, Jin Li, Yonghan Wu

|September 23, 2025

概括

本研究介绍了工业被动光学网络 (PON) 的新策略,以克服数据噪声并改善通信. 拟议的方法显著提高了可靠性,并减少了工业环境中的延迟.

科学领域:

工业通信网络工业通信网络
信号处理信号处理
分布式计算分布式计算

背景情况:

工业被动光学网络 (PON) 对于下一代工业环境中的高速通信至关重要.
复杂的工业环境会产生大量的数据噪声,挑战时间关键的通信和计算卸载.

研究的目的:

为工业PONs提出一个数据噪声免疫计算卸载策略.
在杂的工业环境中提高边缘云协调的可靠性和效率.

主要方法:

集成一个粒子-卡尔曼级联过器 (PKCF) 抑制噪音.
实现一个游戏理论的合作卸载与冷却 (GTCOC) 动态任务分配.
使用纳什平衡调整来平衡任务分配.

主要成果:

PKCF实现了99.9%的噪音抑制精度 (误差率为±0.0003%).
在高密度场景中,GTCOC减少了33.4%的平均延迟,并提高了40%的负载均衡效率.
与DQN和启发式方法相比,提出的方法证明了对工业噪声的优越稳定性和可扩展性.

结论:

开发的策略有效地减轻了数据噪声,并优化了工业PON中的计算卸载.
该方法为在具有挑战性的工业环境中边缘云协调提供了实用和可靠的框架.
这些发现突出了提高工业网络性能和数据完整性的潜力.

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