Joseba Osa;Niclas Björsell;Iñaki Val;Mikel Mendicute
{"title":"Measurement Based Stochastic Channel Model for 60 GHz Mmwave Industrial Communications","authors":"Joseba Osa;Niclas Björsell;Iñaki Val;Mikel Mendicute","doi":"10.1109/OJIES.2023.3334299","DOIUrl":null,"url":null,"abstract":"Communications in the mmWave spectrum are gaining relevance in the last years as they are a promising candidate to cope with the increasing demand of throughput and latency in different use cases. Nowadays, several efforts have been carried out to characterize the propagation medium of these signals with the aim of designing their corresponding communication protocols accordingly, and a wide variety of both outdoor/indoor locations have already been studied. However, very few works endorse industrial scenarios, which are particularly demanding due to their stringent requirements in terms of reliability, determinism, and latency. This work aims to provide an insight of the propagation of 60 GHz mmWave signals in a typical industrial workshop in order to explore the particularities of this kind of scenario. In order to achieve this, an extensive measurement campaign has been carried out in this environment and a stochastic channel model has been proposed and validated.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"4 ","pages":"603-617"},"PeriodicalIF":5.2000,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323142","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10323142/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Communications in the mmWave spectrum are gaining relevance in the last years as they are a promising candidate to cope with the increasing demand of throughput and latency in different use cases. Nowadays, several efforts have been carried out to characterize the propagation medium of these signals with the aim of designing their corresponding communication protocols accordingly, and a wide variety of both outdoor/indoor locations have already been studied. However, very few works endorse industrial scenarios, which are particularly demanding due to their stringent requirements in terms of reliability, determinism, and latency. This work aims to provide an insight of the propagation of 60 GHz mmWave signals in a typical industrial workshop in order to explore the particularities of this kind of scenario. In order to achieve this, an extensive measurement campaign has been carried out in this environment and a stochastic channel model has been proposed and validated.
期刊介绍:
The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments.
Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.