在6G级TDM-PON系统中基于协作分割学习的动态带宽分配
Alaelddin F Y Mohammed1, Yazan M Allawi2, Eman M Moneer3
1Information Technology, Department of International Studies, Dongshin University, 67, Dongshindae-gil, Naju-si 58245, Republic of Korea.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了TDM-PON系统的基于协作分割学习的动态带宽分配 (CSL-DBA). 它提高了流量预测的准确性,并减少了下一代网络的通信开销.
科学领域:
- 电信工程 电信工程 电信工程
- 机器学习应用 机器学习应用
- 网络优化 网络优化
背景情况:
- 时间分割多重复合被动光学网络 (TDM-PON) 需要高效的上游带宽管理.
- 传统的动态带宽分配 (DBA) 方法在动态流量条件下表现不佳.
- 现有的解决方案往往涉及高通讯开销和集中处理.
研究的目的:
- 为TDM-PON系统提出一个新的基于协作分割学习的DBA (CSL-DBA) 框架.
- 为了提高预测性交通适应,并最大限度地减少通讯开销.
- 提高带宽分配对波动流量的响应能力.
主要方法:
- 在光线线终端 (OLT) 和光网络单元 (ONU) 之间实施分割学习 (SL).
- 在ONU本地进行分散的流量分析,仅传输模型更新.
- 在各种交通负载场景 (低,波动,高) 中进行广泛的模拟.
主要成果:
- 实现了超过99%的交通预测准确度.
- 展示了最小的推断延迟和可扩展的学习性能.
- 与联合学习方法相比,通信开销减少了约60%.
结论:
- 在CSL-DBA框架提供了比传统的DBA技术显著改进.
- 拟议的方法在动态的交通环境中非常有效.
- CSL-DBA为下一代6G级TDM-PON系统提供了一个可行的解决方案.
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