{"id":1532,"date":"2020-07-08T14:29:49","date_gmt":"2020-07-08T18:29:49","guid":{"rendered":"https:\/\/cutheme.local\/ips\/?p=1532"},"modified":"2026-03-17T12:44:32","modified_gmt":"2026-03-17T16:44:32","slug":"enabling-wireless-network-big-data-driven-personalization-using-zone-of-tolerance-modeling-and-predictive-analytics","status":"publish","type":"post","link":"https:\/\/research.carleton.ca\/ips\/2020\/enabling-wireless-network-big-data-driven-personalization-using-zone-of-tolerance-modeling-and-predictive-analytics\/","title":{"rendered":"Enabling Wireless Network Big Data-Driven Personalization Using Zone of Tolerance Modeling and Predictive Analytics"},"content":{"rendered":"\n<section class=\"w-screen px-6 cu-section cu-section--white ml-offset-center md:px-8 lg:px-14\">\n    <div class=\"space-y-6 cu-max-w-child-5xl  md:space-y-10 cu-prose-first-last\">\n\n            <div class=\"cu-textmedia flex flex-col lg:flex-row mx-auto gap-6 md:gap-10 my-6 md:my-12 first:mt-0 max-w-5xl\">\n        <div class=\"justify-start cu-textmedia-content cu-prose-first-last\" style=\"flex: 0 0 100%;\">\n            <header class=\"font-light prose-xl cu-pageheader md:prose-2xl cu-component-updated cu-prose-first-last\">\n                                    <h1 class=\"cu-prose-first-last font-semibold !mt-2 mb-4 md:mb-6 relative after:absolute after:h-px after:bottom-0 after:bg-cu-red after:left-px text-3xl md:text-4xl lg:text-5xl lg:leading-[3.5rem] pb-5 after:w-10 text-cu-black-700 not-prose\">\n                        Enabling Wireless Network Big Data-Driven Personalization Using Zone of Tolerance Modeling and Predictive Analytics\n                    <\/h1>\n                \n                                \n                            <\/header>\n\n                    <\/div>\n\n            <\/div>\n\n    <\/div>\n<\/section>\n\n<p>The subject application relates to telecommunication networks and more particularly, to a method and system for managing and allocating wireless network resources to optimize User satisfaction. One aspect of the invention is directed to a system comprising a wireless base station; a user device; 5 and a wireless network connecting said wireless base-station to said user device; said wireless base station being operable: to employ a \u2018zone of tolerance\u2019 to model user satisfaction; and to respond to a request from said user device to access network resources, by allocating network resources based on said \u2018zone of tolerance\u2019 model. Other aspects of the invention are also shown and described including a system and method of allocating network resources based on an automated machine learning model selection and optimization process.<\/p>\n\n\n\n<p>US application 17\/188,683 (int\u2019l filing date 29-Aug-2019)<\/p>\n\n\n\n<p>CA application 3,11,030 (int\u2019l filing date 29-Aug-2019)<\/p>\n\n\n\n<p>EP application 08940587&nbsp; (int\u2019l filing date 29-Aug-2019)<\/p>\n\n\n\n<p>International publication number: WO2020\/0041883<\/p>\n\n\n\n<p>Inventor(s):&nbsp;Alkurd, Rawan; Yanikomeroglu, Halim; Abualhaol, Ibrahim<\/p>\n\n\n\n<p>Please contact <a href=\"https:\/\/research.carleton.ca\/ips\/people\/theresa-white\/\">Theresa White<\/a> for further information.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The subject application relates to telecommunication networks and more particularly, to a method and system for managing and allocating wireless network resources to optimize User satisfaction. One aspect of the invention is directed to a system comprising a wireless base station; a user device; 5 and a wireless network connecting said wireless base-station to said [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[26],"tags":[],"class_list":["post-1532","post","type-post","status-publish","format-standard","hentry","category-licensing-opportunities"],"acf":{"cu_post_thumbnail":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Enabling Wireless Network Big Data-Driven Personalization Using Zone of Tolerance Modeling and Predictive Analytics - Industry and Partnership Services (IPS)<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.carleton.ca\/ips\/2020\/enabling-wireless-network-big-data-driven-personalization-using-zone-of-tolerance-modeling-and-predictive-analytics\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Enabling Wireless Network Big Data-Driven Personalization Using Zone of Tolerance Modeling and Predictive Analytics - Industry and Partnership Services (IPS)\" \/>\n<meta property=\"og:description\" content=\"The subject application relates to telecommunication networks and more particularly, to a method and system for managing and allocating wireless network resources to optimize User satisfaction. 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