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基于机器学习推理的NB-IoT系统的新型反应式禁用方案的性能.

Anastasia Daraseliya1, Eduard Sopin1,2, Julia Kolcheva1

  • 1Department of Probability Theory and Cyber Security, Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., Moscow 117198, Russia.

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概括

本研究介绍了一种基于机器学习的对窄带物联网 (NB-IoT) 系统的禁用方案. 这种新方法通过动态调整流量负载来提高吞吐量,并管理低功率宽带网络 (LPWAN) 的延迟.

关键词:
5G是什么意思? 5G是什么意思?时间延迟延迟延迟延迟延迟延迟mMTCTC 的时间.最佳的资源分配,最优的资源分配随机访问随机访问

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科学领域:

  • 电信工程 电信工程 电信工程
  • 无线通信系统无线通信系统
  • 机器学习应用 机器学习应用

背景情况:

  • 现代的5G+级低功耗广域网 (LPWAN) 技术,如窄带物联网 (NB-IoT),使用多通道插槽ALOHA进行随机访问.
  • 这些系统的随机访问阶段由于流量波动而遭受低吞吐量和不稳定性.
  • 保持最佳吞吐量是具有挑战性的,因为基站 (BS) 缺乏对当前提供的流量负载的了解.

研究的目的:

  • 为使用机器学习 (ML) 的NB-IoT系统提出和分析一种新的反应式禁用方案.
  • 为了证明BS上的碰撞用户设备 (UE) 的数量可以表明交通负载.
  • 描述在拟议的反应式门禁技术下经历的延迟.

主要方法:

  • 使用ML技术来区分物理随机访问通道 (PRACH) 中的事件,基于信号对噪声比 (SNR).
  • 使用XGBoost分类器,准确地将PRACH事件与竞争的UE数量联系起来.
  • 数学描述拟议方案的延迟执行情况.

主要成果:

  • 在区分PRACH事件和估计UE竞争时,ML模型实现了0.98准确度.
  • 拟议方案在超载条件下保持了0.3左右的序言传输成功概率,显著超过传统NB-IoT (小于0.05).
  • 该方案通过动态调整基于流量意识的传输概率,在多通道ALOHA中实现了近乎最佳的吞吐量.

结论:

  • 拟议的基于ML的反应式禁用系统有效地提高了NB-IoT系统的性能.
  • 动态的交通意识和主动的拥堵控制确保有界的延迟,防止系统超载.
  • 这种方法为LPWAN在波动的交通条件下稳定高效运行提供了强大的解决方案.