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关于5G和超越蜂网络中的多参数优化和主动可靠性

Aneeqa Ijaz1, Waseem Raza1, Sajid Riaz1

  • 1AI4Networks Research Center, Department of Electrical & Computer Engineering, University of Oklahoma, Norman, OK 73019, USA.

Sensors (Basel, Switzerland)
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PubMed
概括
此摘要是机器生成的。

本研究介绍了使用离散时间马尔科夫链的6G网络的主动故障预测框架. 它可以早期检测细胞降解,减少停机,提高网络可靠性.

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科学领域:

  • 电信工程 电信工程 电信工程
  • 网络可靠性 网络可靠性
  • 随机模型建模 随机模型建模

背景情况:

  • 在6G中,超密集的异质蜂网络面临着由于错误配置和硬件故障等复杂问题而增加的蜂中断风险.
  • 基于当前的自主网络功能 (ANF) 的故障检测是反应性的,只有在服务质量下降后才能识别问题.

研究的目的:

  • 为下一代无线网络开发积极的故障预测框架.
  • 将网络管理从反应性故障检测转变为主动性故障预测和缓解.

主要方法:

  • 介绍了基于离散时间马尔科夫链 (DTMC) 的新型随机框架.
  • 建模网络可靠性动态,以预测细胞过渡到次优状态.
  • 量化故障到达效应,并确定影响性能的敏感参数.

主要成果:

  • DTMC框架准确地预测了网络故障的时间和可能的原因.
  • 该模型量化了网络处于退化状态的时间部分.
  • 识别了导致性能恶化的敏感网络参数.

结论:

  • 拟议的框架使主动故障管理成为可能,大大减少了电池中断时间.
  • 这种方法提高了下一代无线网络的整体可靠性和弹性.
  • 它为在复杂的网络环境中保持高质量的服务提供了至关重要的能力.