Prediction method of 5G high-load cellular based on BP neural network

Beibei Zhao, Tairan Wu, Fang Fang, Lin Wang, Wenzhang Ren, Xue-bin Yang, Zhangjing Ruan, Xuejin Kou
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引用次数: 2

Abstract

This paper used Back Propagation (BP) Neural Network algorithm to evaluate the network capacity of the 5G cellular, which was based on the daily network traffic of the it. Then the plan can solve the problem of limited 5G capacity in advance, reduce network delay, ensure the network performance and improve users’ perception. We firstly analyze the correlation of KPI indicators that involved in 5G high load cellular to determine strong correlation indicators. Then the BP Neural Network is used to train the KPI sample data and output the simulation results. Finally, according to the result, the cellular network capacity will be evaluated by the result which will also determine whether the cellular has risk of high load.
基于BP神经网络的5G高负荷蜂窝预测方法
本文以5G蜂窝网络的日常网络流量为基础,采用BP神经网络算法对其网络容量进行评估。然后,该方案可以提前解决5G容量有限的问题,减少网络延迟,保证网络性能,提高用户的感知。我们首先对5G高负荷蜂窝所涉及的KPI指标进行相关性分析,确定强相关性指标。然后利用BP神经网络对KPI样本数据进行训练并输出仿真结果。最后,根据结果对蜂窝网络容量进行评估,从而判断蜂窝网络是否存在高负荷风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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