基于机器学习的LTE控制通信延迟根本原因分析和三维建模

S. Mohammed, Muhammad Ilyas
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引用次数: 0

摘要

本研究利用复杂的机器学习进行网络测试,调查和评估LTE控制通信的延迟根本原因分析和3D建模。该研究研究了第五代移动电话的LTE协议,并提供了控制LTE频率的指导方针作为背景知识,尽管它使用了一种不采用LTE标准的独立技术。在100-GHz频段采用512个输入-输出MIMO单元,在6- ghz频段采用128个输入-输出MIMO单元。LOS总是0.5。本文是关于LTE的,而不是使用机器学习的LTE控制路径损失类型通信的3D建模。这项工作的路由损耗取决于交叉波束LTE偏振(±45度)。接收机(Rx)的操作和发射机(Tx)的活动在大约15.25米的高度估计0.5公里的距离上。距离、切换认证、降雨、大气以及低于6ghz和100GHz的天气条件都会影响路径损失。该方法通过提高发射功率和发射效率来增强空间多样性。由于使用开源材料和策略进行规划和开发,在使用MIMO输入/输出接收线进行可疑切换确认的情况下,具有高传输功率和速率,因此可以授权和批准基于ann的LTE频率,用于中频低于6 ghz和100 ghz。该理论将LTE创新维度检验为各种移交验证的无偏性,并允许为三种气候类型的LTE经常性数据传输从6 GHz到100 GHz的各种组织安排设置进行输入边界更改。这个周期应该被视为在各种空气条件下检查LTE网络的不可否认的水平方法。利用MATLAB R2019a中的信号处理工具箱和基于人工智能的显式人工智能人工神经网络计算,可以在具有降雨同化精度和树叶遗漏量的LTE通信级别的数据集中创建三种气候类型的结果答案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Delay Root Cause Analysis and 3D Modeling of LTE Control Communication Using Machine Learning
This study investigates and evaluates delay root cause analysis and 3D modeling of LTE control communication utilizing sophisticated machine learning for network testing. The research studied LTE protocols for 5th-generation mobile telephony and provided guidelines for controlling LTE frequency for background knowledge, although it used an independent technique that did not employ LTE standards. 512 elements of input-output MIMO were employed for 100-GHz and 128 elements for mid-band sub-6-GHz. LOS is always 0.5. This paper is about LTE, not 3D modeling of LTE control path loss type communication using machine learning. This work’s route loss depends on cross-pol beam LTE polarization (±45o). The receiver (Rx) operations and transmitter (Tx) activities in the estimated distance of 0.5 km at an approximate altitude of 15.25 m. Distance, handover authentication, rain, atmosphere, and sub-6GHz vs 100GHz weather conditions affect path loss. The methodology has enhanced the spatial variety by boosting transmitting power and transmitting efficiency. Authorizing and sanctioning ANN-based LTE frequency for both mid-band sub-6-GHz and 100-GHz is possible due to its planning and development using open-source material and strategy with high transmission power and rate under doubtful handover confirmation using MIMO input/yield receiving wires. This theory examines LTE innovation dimensioning as unbiased for various handover verification and allows input boundary alterations for various organization arrangement setups for LTE recurrent data transmission from 6 GHz to 100 GHz for three climate sorts. This cycle should be seen as an undeniable level way to examine LTE networks under various air conditions. Using signal handling tool compartment and explicit AI-based ANN calculation from AI toolkit in MATLAB R2019a, it is possible to create a result answer for three climate types in a dataset with an LTE communication level of exactness of downpour assimilation and abundance foliage miss fort.
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