A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment

IF 3.5 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammad Hossein Zadeh;Marina Barbiroli;Franco Fuschini
{"title":"A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment","authors":"Mohammad Hossein Zadeh;Marina Barbiroli;Franco Fuschini","doi":"10.1109/OJAP.2024.3391835","DOIUrl":null,"url":null,"abstract":"Wireless channel properties in industrial environments can differ from residential or office settings due to the considerable impact of heavy machinery that triggers intricate multipath propagation effects and strong blockage effects. Previous investigations on wireless propagation in factories often consisted of empirical models, that is simple analytical formulas based on measurement data. Unfortunately, they usually lack in flexibility, since they seldom include geometrical parameters describing the industrial scenario and therefore turn out reliable only in industrial scenarios sharing the same propagation characteristics as those where the measurements were performed. In response to this limitation, this article harnesses the power of Machine Learning to model propagation markers like path loss, shadowing, and delay spread in the industrial environment. By employing Machine Learning techniques, the objective is to achieve flexibility and adaptability in modeling, enabling the system to effectively generalize across diverse industrial scenarios. The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. Results are in fair overall agreement with previous studies and highlight some trends about the sensitivity of the propagation parameters to the considered input features.","PeriodicalId":34267,"journal":{"name":"IEEE Open Journal of Antennas and Propagation","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506246","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Antennas and Propagation","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10506246/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Wireless channel properties in industrial environments can differ from residential or office settings due to the considerable impact of heavy machinery that triggers intricate multipath propagation effects and strong blockage effects. Previous investigations on wireless propagation in factories often consisted of empirical models, that is simple analytical formulas based on measurement data. Unfortunately, they usually lack in flexibility, since they seldom include geometrical parameters describing the industrial scenario and therefore turn out reliable only in industrial scenarios sharing the same propagation characteristics as those where the measurements were performed. In response to this limitation, this article harnesses the power of Machine Learning to model propagation markers like path loss, shadowing, and delay spread in the industrial environment. By employing Machine Learning techniques, the objective is to achieve flexibility and adaptability in modeling, enabling the system to effectively generalize across diverse industrial scenarios. The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. Results are in fair overall agreement with previous studies and highlight some trends about the sensitivity of the propagation parameters to the considered input features.
工业环境中无线传播建模的机器学习方法
工业环境中的无线信道特性可能不同于住宅或办公室环境,这是因为重型机械的巨大影响会引发复杂的多径传播效应和强烈的阻塞效应。以往对工厂中无线传播的研究通常包括经验模型,即基于测量数据的简单分析公式。遗憾的是,这些模型通常缺乏灵活性,因为它们很少包含描述工业场景的几何参数,因此只有在与测量数据具有相同传播特性的工业场景中,这些模型才是可靠的。针对这一局限性,本文利用机器学习的强大功能,对工业环境中的路径损耗、阴影和延迟传播等传播标记进行建模。通过采用机器学习技术,目的是实现建模的灵活性和适应性,使系统能够有效地在不同的工业场景中通用。建议的模型依赖于预测算法的组合,包括线性回归模型和多层感知器,它们协同工作来模拟所考虑的传播标记与频率、机器尺寸、间距和密度等输入特征之间的关系。研究结果与之前的研究结果基本一致,并突出了传播参数对所考虑的输入特征的敏感性的一些趋势。
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
6.50
自引率
12.50%
发文量
90
审稿时长
8 weeks
×
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