移交
计算机科学
信号(编程语言)
GSM演进的增强数据速率
信号强度
机动性管理
阴影贴图
隐藏物
人工神经网络
人工智能
功率(物理)
实时计算
机器学习
计算机网络
物理
无线传感器网络
程序设计语言
量子力学
作者
Shaoen Wu,Junwei Ren,Tiezhu Zhao,Yue Wang
标识
DOI:10.1109/vtc2021-fall52928.2021.9625539
摘要
This paper addressed mobile edge computing (MEC) mobility management using machine learning methods. The mobility decision was based on a reference signal received power (RSRP) value and uncertainty predictor. Neural networks (NNs) were used to establish the predictor, which output the RSRP mean values and standard deviations of different neighbor cells. Closed-form expressions of handover probabilities were derived. With these probabilities, the MEC server was able to cache user services in advance in order to minimize disruptions during handover. Real field data were collected in a typical dense urban area and used to evaluate the performance of the proposed algorithm.
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