[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121556-en":3,"doc-seo-121556-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121556,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning","Wireless communications exhibit unpredictability, complicating the delivery of consistent link quality. This work analyzes prediction models with the goal of generating accurate and efficient Wi‑Fi link quality forecasts using machine learning. It focuses on data-driven approaches built from linear combinations of exponential moving averages, enabling low-complexity implementations suitable for hardware with limited processing. Experimental evaluation uses a real-world Wi‑Fi testbed with both channel-dependent and channel-independent training datasets, with channel-independent models showing competitive performance for manufacturer-generalized training.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nAccurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning  \nOriginal  \nAccurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning / Formis, Gabriele; Cena, Gianluca; Wisniewski, Lukasz; Scanzio, Stefano. -In: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. -ISSN 1551- 3203. -22:2(2026), pp. 763-774. [10 . 1109/tii.2025.3609224]  \nAvailability:  \nThis version is available at: 11583/3004399 since: 2025-10-23T13:29:31Z  \nPublisher:  \nInstitute of Electrical and Electronics Engineers  \nPublished  \nDOI:10.1109/tii.2025.3609224  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n21 February 2026  \nIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 22, NO. 2, FEBRUARY 2026 763  \nAccurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning  \nGabriele Formis , Member, IEEE, Gianluca Cena, Senior Member, IEEE, Lukasz Wisniewski , Senior Member, IEEE, and Stefano Scanzio, Senior Member, IEEE  \nAbstract—Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This article presents a comprehensive analysis of various prediction models, with a focus on achieving accurate and efficient Wi-Fi link quality forecasts using machine learning techniques. Specifically, the article evaluates the performance of data-driven models based on the linear combination of exponential moving averages, which are designed for low-complexity implementations and are then suitable for hardware platforms with limited processing resources. Accuracy of the proposed approaches was assessed using experimental data from a real-world Wi-Fi testbed, considering both channel-dependent and channel-independent training data. Remarkably, channel-independent models, which allow for generalized training by equipment manufacturers, demonstrated competitive performance. Overall, this study provides insights into the practical deployment of machine learning-based prediction models for enhancing Wi-Fi dependability in industrial environments.  \nIndex Terms—Channel quality prediction, data-driven models, exponential moving average (EMA), machine learning (ML), Wi-Fi.  \nI. INTRODUCTION  \nWIRELESS communications suffer from disturbance due  \nto the open nature ofthe transmission medium, including interference from nearby communication equipment operating in the same frequency range and electromagnetic noise generated by power equipment, which may abound in industrial plants [1] . These phenomena impact on frame transmission attempts tangibly, to the point that their outcomes(either successor failure) can be modeled as binary random variables, whose  \nReceived 27 September 2024; revised 19 December 2024, 21 July 2025, 11 August 2025, and 28 August 2025; accepted 1 September 2025. Date of publication 15 October 2025; date of current version 5 February 2026 . This work was supported in part by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on “Telecommunications of the Future”under Grant PE00000001-program “RESTART”. Paper no. TII-24-5051 .(Corresponding author: Stefano Scanzio.)  \nGabriele Formis is with the Politecnico di Torino, 10129 Torino, Italy, and also with the CNR-IEIIT, National Research Council of Italy, 00185 Roma, Italy (e-mail: gabriele.formis@polito.it) .  \nGianluca Cena and Stefano Scanzio are with the CNR-IEIIT, National Research Council of Italy, 00185 Roma, Italy (e-mail: gian[luca.cena@cnr.it](luca.cena@cnr.it) ; [stefano.scanzio@cnr.it](stefano.scanzio@cnr.it)) .  \nLukasz Wisniewski is with the Institute Industrial IT-inIT, Technische Hochschule OWL, 32657 Lemgo, Germany (e-mail: [lukasz.wisniewski@th-owl.de](lukasz.wisniewski@th-owl.de)) .  \nDigital Objec","cbCait0dvdPPfCTw","https://ap.wps.com/l/cbCait0dvdPPfCTw","pdf",2005932,1,13,"English","en",105,"# Introduction\n## Motivation for Wi-Fi link quality prediction\n## Limitations of existing wireless determinism and ARQ\n# Problem context and industrial relevance","[{\"question\":\"Why is Wi‑Fi link quality prediction important in industrial wireless networks?\",\"answer\":\"Wireless transmissions are affected by interference and electromagnetic noise, leading to time-varying and nonstationary channel conditions. This unpredictability can cause unreliable behavior that industrial applications typically cannot tolerate.\"},{\"question\":\"What machine learning approach does the paper emphasize for low-complexity implementation?\",\"answer\":\"The study evaluates data-driven models based on linear combinations of exponential moving averages (EMAs). These are designed to keep computational complexity low and be practical for limited-resource hardware.\"},{\"question\":\"How does the paper compare channel-dependent and channel-independent training?\",\"answer\":\"Performance is assessed using experimental data from a real-world Wi‑Fi testbed with both training types. Channel-independent models, which enable generalized training by equipment manufacturers, achieve competitive results.\"}]","Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning | PDF",1785736228,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"accurate-and-efficient-prediction-of-wi-fi-link-quality-based-on-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/accurate-and-efficient-prediction-of-wi-fi-link-quality-based-on-machine-learning/121556/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is Wi‑Fi link quality prediction important in industrial wireless networks?","Question",{"text":75,"@type":76},"Wireless transmissions are affected by interference and electromagnetic noise, leading to time-varying and nonstationary channel conditions. This unpredictability can cause unreliable behavior that industrial applications typically cannot tolerate.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning approach does the paper emphasize for low-complexity implementation?",{"text":80,"@type":76},"The study evaluates data-driven models based on linear combinations of exponential moving averages (EMAs). These are designed to keep computational complexity low and be practical for limited-resource hardware.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper compare channel-dependent and channel-independent training?",{"text":84,"@type":76},"Performance is assessed using experimental data from a real-world Wi‑Fi testbed with both training types. Channel-independent models, which enable generalized training by equipment manufacturers, achieve competitive results.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]