[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126050-en":3,"doc-seo-126050-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126050,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Optimization of Geographical Fingerprinting for 5G Networks Using Machine Learning - Master Thesis","This thesis enhances 5G network optimization through a Machine Learning-driven approach to geographical fingerprinting. Leveraging Minimization of Drive Tests (MDT) and User Equipment (UE) data, it improves positioning accuracy across network sectors with distinct density and mobility patterns, spanning urban and rural environments. It refines a weighted k-Nearest Neighbors (WKNN) method tailored to heterogeneous conditions. The work further introduces synthetic datasets generated with generative AI to mitigate sparse-data constraints. The resulting training improves localization accuracy especially in sparse areas while reducing dependence on extensive ground-truth collection, supporting both theoretical and practical next-generation wireless network management.","ALMA MATER STUDIORUM  \nUNIVERSITÀ DI BOLOGNA  \nDEPARTMENT OF COMPUTER SCIENCE  \nAND ENGINEERING  \nARTIFICIAL INTELLIGENCE  \nMASTER THESIS  \nin  \nMOBILE RADIO NETWORKS M  \nOPTIMIZATION OF GEOGRAPHICAL FINGERPRINTING FOR 5G NETWORKS  \nUSING MACHINE LEARNING  \nCANDIDATE SUPERVISOR  \nTony Chahoud Prof. Roberto Verdone  \nCO-SUPERVISORSIng. Marco Skocaj  \nIng. Lorenzo Mario Amorosa  \nAcademic year 2024-2025  \nSession 3rd  \nAbstract  \nThis thesis enhances network optimization for 5G networks through a Machine Learning (ML)-driven approach to geographical fingerprinting. In collaboration with WiLab anda globally renowned telecommunications company, the study utilizes Minimization of Drive Tests (MDT) to collect vital data from User Equipment (UE), crucial for improving positioning accuracy in network sectors with diverse density and mobility patterns, from urban to rural environments. A key advancement is the refined application of the weighted k-Nearest Neighbors (WKNN) algorithm, specifically adapted to enhance localization in varied settings, optimizing performance across the spectrum of network conditions.  \nA significant innovation of this work is the use of synthetic data generated through generative AI (Gen-AI) models to address the limitations of sparse data in challenging environments. By creating high-fidelity synthetic datasets, the study simulates realistic network scenarios, enhancing model training without extensive ground-truth data. This approach significantly improves positioning accuracy within 5G networks, particularly insparser areas, and reduces reliance on traditional data collection methods.  \nThis thesis contributes to both theoretical advancements in network management and practical applications in deploying ML to enhance next-generation wireless networks. The methodologies developed are poised to influence future advancements in 5G technologies, paving the way for more robust and efficient network services.  \nAcknowledgements  \nI would like to express my deepest gratitude tomy university supervisor, Professor Roberto Verdone, for his unwavering support and guidance throughout my academic journey. From being a student in one of his elective courses, to an intern at WiLab where I conducted the research for this thesis, and finally to becoming an employee at WiLab, I am immensely grateful for his trust and the invaluable opportunities he provided me. When I think about turning points in life, I think of him, as his mentorship has been instrumental in shaping the direction of my career beyond academia as a researcher.  \nI am also profoundly grateful to my co-supervisor, Ing.Marco Skocaj, for his willingness to share his knowledge through constructive dialogues, which helped me delve deeper into many telecommunication-related challenges during my internship. A special thanks goes to my friend and colleague, Ing.Lorenzo Amorosa, for his daily support and encouragement. Working alongside him on various projects was a rewarding experience, and his guidance during the writing process of this thesis was invaluable.  \nI would like to extend my thanks to the entire WiLab team, an inspiring group and a true hub of innovation. I am proud to be a part of such an amazing team and look forward to contributing to more groundbreaking ideas and projects together in the future.  \nMy heartfelt appreciation also goes to my friends in Bologna. They made all the difference in my experience abroad, turning my journey into a memorable adventure. Living, sharing memories, and spending every moment with them filled the gap of being away from home. I will never forget the first day I arrived in Bologna three years ago, alone and without knowing anyone. I am grateful that during these three years, I had the chance to meet such wonderful people who became an integral part of my life.  \nFinally, from the bottom of my heart, I wish to express my gratitude to my beloved family in my cherished yet struggling country, Lebanon. Mom and Dad, Thank","cbCailOn4JVW3N3M","https://ap.wps.com/l/cbCailOn4JVW3N3M","pdf",28108462,7,1,75,"English","en",105,"# Introduction\n# State of the Art\n## Positioning Technologies in 5G Networks\n## Fingerprinting\n## Machine Learning Application in Positioning\n# Techniques and Methodology\n## MDT Data\n## WKNN\n## Model Implementation Strategy\n## Data Augmentation\n# Results\n## Densiy Effect\n## GPS and Environmental Mobility Effects\n## Synthetic Data’s Impact\n# Conclusions\n# Bibliography\n# Appendix","[{\"question\":\"How does the thesis use MDT and UE data to improve 5G positioning?\",\"answer\":\"It collects vital positioning-related information using Minimization of Drive Tests (MDT) with User Equipment (UE), then uses that data to improve localization accuracy across sectors with varying density and mobility patterns.\"},{\"question\":\"What is the key machine learning contribution regarding WKNN?\",\"answer\":\"The thesis advances localization by applying a refined weighted k-Nearest Neighbors (WKNN) algorithm, adapted to work effectively under diverse network conditions.\"},{\"question\":\"Why does the thesis introduce generative AI-based synthetic data, and what effect does it have?\",\"answer\":\"It addresses limitations caused by sparse data in challenging environments by generating high-fidelity synthetic datasets, which improves model training and increases positioning accuracy while reducing reliance on extensive ground-truth data.\"}]","Optimization of Geographical Fingerprinting for 5G Networks Using Machine Learning - Master Thesis | PDF",1785902781,189,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"optimization-of-geographical-fingerprinting-for-5g-networks-using-machine-learning-master-thesis","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/optimization-of-geographical-fingerprinting-for-5g-networks-using-machine-learning-master-thesis/126050/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"How does the thesis use MDT and UE data to improve 5G positioning?","Question",{"text":77,"@type":78},"It collects vital positioning-related information using Minimization of Drive Tests (MDT) with User Equipment (UE), then uses that data to improve localization accuracy across sectors with varying density and mobility patterns.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What is the key machine learning contribution regarding WKNN?",{"text":82,"@type":78},"The thesis advances localization by applying a refined weighted k-Nearest Neighbors (WKNN) algorithm, adapted to work effectively under diverse network conditions.",{"name":84,"@type":75,"acceptedAnswer":85},"Why does the thesis introduce generative AI-based synthetic data, and what effect does it have?",{"text":86,"@type":78},"It addresses limitations caused by sparse data in challenging environments by generating high-fidelity synthetic datasets, which improves model training and increases positioning accuracy while reducing reliance on extensive ground-truth data.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]