Modeling ML Efficiency in Telecommunications

Modeling ML Efficiency in Telecommunications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling ML Efficiency in Telecommunications Julio Corona, Rafael Teixeira, Mário Antunes, Rui L. Aguiar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4351759/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Machine learning (ML) models are envisioned to play a fundamental role in Beyond 5G networks. However, training these models presents significant challenges due to the vast amounts of data involved and the complexity of the algorithms, requiring substantial training time and computing resources. This study offers a comprehensive analysis of how dataset characteristics, model complexity, and the number of iterations influence model performance and training time. Furthermore, we take pioneering steps towards modeling the performance and training time of ML models, specifically within the telecommunications sector. Our findings can pave the way for informed decision-making processes in telecommunications, enabling a comprehensive understanding of expected performance and training time requirements. Leading to reduced development times and optimized energy consumption and resource allocation. Machine Learning Performance Execution Time Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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