基于加权模糊C-Mean和三向决策理论的5G-TSN融合网络的动态QoS映射算法
Yuhang Wu1, Fangmin Xu1, Lina Ning2
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了5G时间敏感网络 (TSN) 网络的动态加权服务质量 (QoS) 映射方法. 这种新的方法确保了一致的QoS,并改善了融合网络中的负载平衡.
科学领域:
- * 网络工程 网络工程
- * 电信 电信服务 电信服务
背景情况:
- *在融合的5G时间敏感网络 (TSN) 环境中确保端到端的服务质量 (QoS) 存在重大挑战.
- *现有的方法可能会与动态网络负载和复杂的QoS属性管理作斗争.
研究的目的:
- *为5G-TSN融合网络提出一种新的动态加权QoS映射方法.
- * 增强端到端的 QoS 一致性并优化负载平衡.
- * 开发一种适应性系统,能够处理不同的网络条件.
主要方法:
- *利用权重模糊C-Means (WFCM) 基于QoS属性对时间敏感网络 (TSN) 流进行集群,减少计算复杂性.
- *采用基于决策的三向方法,为TSN流集群分配适当的5G QoS标识符 (5QI) 值.
- * 实施了动态权重调整,考虑到QoS相似性和适应性网络管理的剩余负载率.
主要成果:
- * 拟议的WFCM-TDwQM方法与其他映射算法组合相比,证明了优越的端到端的QoS一致性.
- * 在多样化和变化的网络负载下实现了更好的负载平衡性能.
- *在不同的网络场景中评估和确认了有效的映射性能.
结论:
- * WFCM-TDwQM方法有效地解决了5G-TSN融合网络中的QoS管理的挑战.
- * 动态加权机制允许对网络负载波动进行强大的适应.
- * 这种方法为在下一代网络中保持 QoS 和负载平衡提供了有希望的解决方案.
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