Parameter Estimation of Cellular Communication Systems Models in Computational MATLAB Environment: A Systematic Solver-based Numerical Optimization Approaches

Q1 Mathematics
J. Isabona, Sayo A. Akinwumi, Theophilus E. Arijaje, Odesanya Ituabhor, A. Imoize
{"title":"Parameter Estimation of Cellular Communication Systems Models in Computational MATLAB Environment: A Systematic Solver-based Numerical Optimization Approaches","authors":"J. Isabona, Sayo A. Akinwumi, Theophilus E. Arijaje, Odesanya Ituabhor, A. Imoize","doi":"10.5815/ijcnis.2024.03.06","DOIUrl":null,"url":null,"abstract":"Model-based parameter estimation, identification, and optimisation play a dominant role in many aspects of physical and operational processes in applied sciences, engineering, and other related disciplines. The intricate task involves engaging and fitting the most appropriate parametric model with nonlinear or linear features to experimental field datasets priori to selecting the best optimisation algorithm with the best configuration. Thus, the task is usually geared towards solving a clear optimsation problem. In this paper, a systematic-stepwise approach has been employed to review and benchmark six numerical-based optimization algorithms in MATLAB computational Environment. The algorithms include the Gradient Descent (GRA), Levenberg-Marguardt (LEM), Quasi-Newton (QAN), Gauss-Newton (GUN), Nelda-Meald (NEM), and Trust-Region-Dogleg (TRD). This has been accomplished by engaging them to solve an intricate radio frequency propagation modelling and parametric estimation in connection with practical spatial signal data. The spatial signal data were obtained via real-time field drive test conducted around six eNodeBs transmitters, with case studies taken from different terrains where 4G LTE transmitters are operational. Accordingly, three criteria in connection with rate of convergence Results show that the approximate hessian-based QAN algorithm, followed by the LEM algorithm yielded the best results in optimizing and estimating the RF propagation models parameters. The resultant approach and output of this paper will be of countless assets in assisting the end-users to select the most preferable optimization algorithm to handle their respective intricate problems.","PeriodicalId":36488,"journal":{"name":"International Journal of Computer Network and Information Security","volume":" 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Network and Information Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5815/ijcnis.2024.03.06","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

Abstract

Model-based parameter estimation, identification, and optimisation play a dominant role in many aspects of physical and operational processes in applied sciences, engineering, and other related disciplines. The intricate task involves engaging and fitting the most appropriate parametric model with nonlinear or linear features to experimental field datasets priori to selecting the best optimisation algorithm with the best configuration. Thus, the task is usually geared towards solving a clear optimsation problem. In this paper, a systematic-stepwise approach has been employed to review and benchmark six numerical-based optimization algorithms in MATLAB computational Environment. The algorithms include the Gradient Descent (GRA), Levenberg-Marguardt (LEM), Quasi-Newton (QAN), Gauss-Newton (GUN), Nelda-Meald (NEM), and Trust-Region-Dogleg (TRD). This has been accomplished by engaging them to solve an intricate radio frequency propagation modelling and parametric estimation in connection with practical spatial signal data. The spatial signal data were obtained via real-time field drive test conducted around six eNodeBs transmitters, with case studies taken from different terrains where 4G LTE transmitters are operational. Accordingly, three criteria in connection with rate of convergence Results show that the approximate hessian-based QAN algorithm, followed by the LEM algorithm yielded the best results in optimizing and estimating the RF propagation models parameters. The resultant approach and output of this paper will be of countless assets in assisting the end-users to select the most preferable optimization algorithm to handle their respective intricate problems.
计算 MATLAB 环境中蜂窝通信系统模型的参数估计:基于求解器的系统数值优化方法
基于模型的参数估计、识别和优化在应用科学、工程学和其他相关学科的物理和操作过程的许多方面发挥着主导作用。这项复杂的任务包括在选择具有最佳配置的最佳优化算法之前,将具有非线性或线性特征的最合适参数模型与现场实验数据集进行关联和拟合。因此,这项任务通常是为了解决一个明确的优化问题。本文在 MATLAB 计算环境中采用了一种系统的逐步方法,对六种基于数值的优化算法进行了评测和基准测试。这些算法包括梯度下降算法(GRA)、Levenberg-Marguardt 算法(LEM)、准牛顿算法(QAN)、高斯-牛顿算法(GUN)、Nelda-Meald 算法(NEM)和信任区域-狗腿算法(TRD)。通过让它们结合实际空间信号数据解决复杂的无线电频率传播建模和参数估计问题,实现了这一目标。空间信号数据是通过围绕六个 eNodeB 发射机进行的实时现场驱动测试获得的,案例研究来自 4G LTE 发射机运行的不同地形。因此,与收敛速度相关的三个标准结果表明,在优化和估计射频传播模型参数方面,基于近似哈希值的 QAN 算法和 LEM 算法取得了最佳结果。本文的方法和成果将为最终用户选择最合适的优化算法来处理各自的复杂问题提供无数帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.10
自引率
0.00%
发文量
33
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信