[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124764-en":3,"doc-seo-124764-105":30,"detail-sidebar-cat-0-en-105":95},{"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},124764,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine Learning for Wireless Network Throughput Prediction - Research Focus","This paper analyzes a dataset of radio frequency (RF) measurements and key performance indicators (KPIs) collected at 1876.6 MHz with 10 MHz bandwidth from an operational 4G LTE network in Nigeria. Metrics include RSRP, RSRQ, RSSI, and SINR, computed from three eNodeBs. After careful cleaning, a 20-minute subset from one serving eNB is used to predict PDCP DL Throughput. Linear Regression and Random Forest are compared using MAE and RMSE, with Random Forest delivering stronger predictive performance.","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \n\n| School of Mathematical and Statistical\u003Cbr>Sciences Faculty Publications and\u003Cbr>Presentations | College of Sciences |\n| --- | --- |\n\n2023  \nMachine Learning for Wireless Network Throughput Prediction Gustavo A. Fernandez  \nFollow this and additional works at: [https://scholarworks.utrgv.edu/mss_fac](https://scholarworks.utrgv.edu/mss_fac)  \n Part of the Mathematics Commons  \n\n|  |  |\n| --- | --- |\n|  |  |\n|  􀂦  The University of Texas Rio Grande Valley |  |\n| \u003Cbr> Wireless Network, Machine Learning, Regression, Random Forest  August 16th, 2023\u003Cbr> [https://doi.org/10.21203/rs.3.rs-3267046/v1](https://doi.org/10.21203/rs.3.rs-3267046/v1)\u003Cbr> 􀁱 􀅏 This work is licensed under a Creative Commons Attribution 4.0 International License.\u003Cbr>Read Full License\u003Cbr> No competing interests reported. |  |\n\nMachine Learning for Wireless Network Throughput Prediction  \nGustavo A Fernandez  \nThe University of Texas Rio Grande Valley.  \nAbstract  \nThis paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Power), which measures the power level of reference signals; RSRQ (Reference Signal Received Quality), an indicator of signal quality that provides insight into the number of users sharing the same resources; RSSI (Received Signal Strength Indicator), which gauges the total received power in a bandwidth; SINR (Signal to Interference plus Noise Ratio), a measure of signal quality considering both interference and noise; and other KPIs, all derived from three evolved node base stations (eNodeBs) . After meticulous data cleaning, a subset of measurements from one serving eNB, spanning a 20-minute duration, was selected for deeper analysis. The PDCP DL Throughput, as a vital KPI metric, plays a paramount role in evaluating network quality and resource allocation strategies. Leveraging the high granularity of the data, the primary aim was to predict throughput. For this purpose, I compared the predictive capabilities of two machine learning models: Linear Regression and Random Forest. Metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) were used to examine the models as they o􀀋er a comprehensive insight into the models accuracies. The comparative analysis highlighted the superior performance of the Random Forest model in predicting the PDCP DL Throughput. The insights derived from this research can potentially guide network engineers and data scientists in optimizing network performance, ensuring a seamless user experience. Furthermore, as the telecommunication industry advances towards the integration of 5G and beyond, the methodologies explored in this paper will be invaluable in addressing the increasingly complex challenges of future wireless networks.  \nKeywords: Wireless Network, Machine Learning, Regression, Random Forest  \n1  \n1 Introduction  \nIn today’s digital age, telecommunications stands as a cornerstone of global connectivity. As the world becomes increasingly interconnected, cellular network operators grapple with the relentless challenge of accommodating escalating user demands. The explosion in media consumption, especially with the introduction of bandwidth-intensive applications, real-time media streaming on social platforms, and the rapidly evolving realm of connected and autonomous vehicles, has placed unprecedented pressure on network resources. To address these challenges, operators are in a continuous quest for cutting-edge solutions. One of the primary objectives is to re􀀌ne resource allocation and load balancing mechanisms, ensuring that networks can handle the ever-growing data tra􀀎c without compromising on performance. The anticipatory approach to resource allocation and network management is a groundbreaking paradigm that","cbCaifjChEcxHR9d","https://ap.wps.com/l/cbCaifjChEcxHR9d","pdf",337906,1,11,"English","en",105,"# Abstract\n# Introduction\n## Network resource allocation and load balancing\n## Predictive resource allocation and QoS\n## Related work\n# Dataset and KPI definitions\n## RF measurements and KPIs\n# Data preprocessing and feature selection\n# Predictive models and evaluation\n## Linear Regression vs. Random Forest\n## MAE and RMSE metrics\n# Results and implications","[{\"question\":\"What dataset and network context are used for throughput prediction?\",\"answer\":\"The study uses RF measurements and KPIs at 1876.6 MHz with 10 MHz bandwidth from an operational 4G LTE network in Nigeria, including data from three eNodeBs.\"},{\"question\":\"Which throughput metric is the main prediction target?\",\"answer\":\"The paper focuses on predicting PDCP DL Throughput as the key KPI for evaluating network quality and resource allocation.\"},{\"question\":\"Which machine learning models are compared, and how are they evaluated?\",\"answer\":\"The work compares Linear Regression and Random Forest, evaluating performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).\"},{\"question\":\"What result does the comparison of models show?\",\"answer\":\"Random Forest outperforms Linear Regression in predicting PDCP DL Throughput, producing more accurate throughput estimates.\"}]","Machine Learning for Wireless Network Throughput Prediction - Research Focus | PDF",1785894378,28,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-for-wireless-network-throughput-prediction-research-focus","",{"@graph":36,"@context":89},[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/machine-learning-for-wireless-network-throughput-prediction-research-focus/124764/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What dataset and network context are used for throughput prediction?","Question",{"text":75,"@type":76},"The study uses RF measurements and KPIs at 1876.6 MHz with 10 MHz bandwidth from an operational 4G LTE network in Nigeria, including data from three eNodeBs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which throughput metric is the main prediction target?",{"text":80,"@type":76},"The paper focuses on predicting PDCP DL Throughput as the key KPI for evaluating network quality and resource allocation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are compared, and how are they evaluated?",{"text":84,"@type":76},"The work compares Linear Regression and Random Forest, evaluating performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).",{"name":86,"@type":73,"acceptedAnswer":87},"What result does the comparison of models show?",{"text":88,"@type":76},"Random Forest outperforms Linear Regression in predicting PDCP DL Throughput, producing more accurate throughput estimates.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]