在5G新无线电上生成实时调度数据集
Xi Jin1,2,3, Haoxuan Chai1,2,3,4, Changqing Xia1,2,3
1Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
本研究引入了使用优化模块理论 (OMT) 和满足模块理论 (SMT) 的新方法,以生成5G新无线电 (NR) 调度的训练数据. 这使得监督学习能够提高工业无线性能.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 无线通信无线通信
背景情况:
- 5G新的无线电 (NR) 系统提供先进的工业无线运动控制.
- 现有的实时调度算法与复杂的5G NR调度模型不兼容,限制了资源的可用性.
- 监督学习对复杂的调度有希望,但缺乏适合5G NR的适当培训数据集.
研究的目的:
- 开发用于生成5G NR调度培训数据集的方法.
- 实现监督学习的应用,以优化5G NR对工业系统的资源配置.
主要方法:
- 提出了两种方法:一种基于优化模块理论 (OMT) 的方法,另一种更有效的方法是基于可满足性模块理论 (SMT).
- 为了加快解决器性能,OMT方法被优化为更少的变量.
- 通过使用特定的定理和算法,通过收紧搜索空间,SMT方法进一步减少了解决时间.
主要成果:
- 与现有方法相比,基于SMT的方法实现了74.7%的解决方案时间缩短.
- 在生成的数据集上训练的监督学习模型表现出比传统的多项式时间算法更好的调度性能.
结论:
- 开发的OMT和SMT方法有效地生成5G NR调度的培训数据集.
- 由这些数据集驱动的监督学习为工业应用中复杂的5G NR调度挑战提供了可行且高性能的解决方案.
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