[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125755-en":3,"doc-seo-125755-105":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},125755,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","ENHANCING CIRCULAR MICROSTRIP PATCH ANTENNA PERFORMANCE USING MACHINE LEARNING MODELS","Machine learning is expected to play a major role in next-generation wireless communication networks by improving coverage and spectrum efficiency. This paper presents an ML-driven workflow for designing and optimizing a circular microstrip patch antenna. Six supervised ML models are trained to predict antenna return loss (S11), and their predictive performance is evaluated. Results indicate that all models achieve effective accuracy, with KNN reaching the highest accuracy at 98.5%. The approach can accelerate antenna design while enabling better optimization for emerging 5G, 6G, IoT, and flexible wireless systems.","Original scientific paper  \nENHANCING CIRCULAR MICROSTRIP PATCH ANTENNA PERFORMANCE USING MACHINE LEARNING MODELS  \nRachit Jain, Vandana Vikas Thakare, P.K. Singhal  \nDepartment of Electronics Engineering, Madhav Institute of Technology & Science,  \nGwalior, M.P, India  \nAbstract. Machine learning (ML) will be heavily used in the future generation of wireless communication networks. The development of diverse communication-based applications is expected to boost coverage and spectrum efficiency in relation to conventional systems.  \nML may be employed to develop solutions in a wide range of domains, such as antennas.  \nThis article describes the design and optimization of a circular patch antenna. The optimization is done through ML algorithms. Six ML models, Decision Tree, Random Forest, XG-Boost Regression, K-Nearest Neighbour (KNN), Gradient Boosting Regression (GBR), and Light Gradient Boosting Regression (LGBR), were employed in this work to predict the antenna's return loss (S11). The findings show that all of these models work well, with KNN having the highest accuracy in predicting return loss of 98.5%. The antenna design & optimization process can be accelerated with the support of ML. These developments allow designers to push beyond the limits of antenna technology, optimize performance, and offer novel solutions for emerging applications such as 5G, 6G, IoT, and flexible wireless communication systems).  \nKey words: Circular patch antenna, Machine Learning (ML), Return Loss (S11), KNN, Decision Tree, Random Forest, XG Boost, GBR, LGBR  \n1. INTRODUCTION  \nAntennas were originally used only for receiving communications such as radio and television. Antennas are now found in almost every electronic gadget and are extremely important. The need for fast and dependable communication networks has been rising rapidly over the past several years. The employing of ultra-wideband (UWB) antennas isone method that could be used to accomplish this. The frequency range between 3.1 and 10.6 GHz has been designated by the Federal Communications Commission (FCC) for UWB applications [1, 2] . Since then, several researchers have started working on optimized antennas for various UWB applications. For the development and optimization of antennas,  \nReceived June 30, 2023; revised August 06, 2023, August 26, 2023 and August 31, 2023; accepted September 05, 2023 Corresponding author: Rachit Jain  \nDepartment of Electronics Engineering, Madhav Institute of Technology & Science, Gwalior, M.P, India  \nE-mail: [rachit2709@gmail.com](rachit2709@gmail.com)  \n© 2023 by University of Niš, Serbia | Creative Commons License: CC BY-NC-ND  \nelectromagnetic (EM) simulators such as the High-Frequency Structure Simulator (HFSS) are commonly used. To achieve the desired parameters, the optimization will be done by adjusting the size of various antenna attributes. Usually, the test-and-error approach has been used to carry out the optimization process. That is why the optimizing procedure consumes alot of time. Traditional antenna design methodologies rely significantly on the practical experiences of designers and electromagnetic (EM) simulation technologies. However, these approaches are time-consuming, computationally expensive, and sometimes produce suboptimal results. As a result, there is a great demand for more efficient and intelligent methodologies for designing and optimizing antennas for a wide range of applications [3] .  \nDue to the diverse shapes of antennas, exact solutions in finite and closed forms are not available. However, by approximating solutions, valuable insights can be gained for antenna design. Numerical analysis is a widely adopted technique for antenna design. Methods such as finite difference time domain, finite element method electromagnetic, and Method of Moments [4, 5, 6,] are commonly utilized for testing and evaluating antennas. In complex antenna designs, this approach posed challenges in terms of memory usage and CPU ","cbCaiadz6tRB3BLG","https://ap.wps.com/l/cbCaiadz6tRB3BLG","pdf",695334,1,12,"English","en",105,"# Introduction\n## Motivation for ML in antenna design\n## Limitations of conventional EM simulation and trial-and-error\n# ML-based circular patch antenna design\n## ML models used to predict return loss (S11)\n## Optimization process and accuracy results\n# Outcomes and applications","[{\"question\":\"What is the main goal of this paper?\",\"answer\":\"To design and optimize a circular microstrip patch antenna using machine learning models that can predict return loss (S11).\"},{\"question\":\"Which machine learning models are used to predict S11?\",\"answer\":\"Decision Tree, Random Forest, XG-Boost Regression, K-Nearest Neighbour (KNN), Gradient Boosting Regression (GBR), and Light Gradient Boosting Regression (LGBR).\"},{\"question\":\"How accurate is the best-performing model?\",\"answer\":\"KNN provides the highest accuracy, achieving 98.5% in predicting return loss (S11).\"}]","ENHANCING CIRCULAR MICROSTRIP PATCH ANTENNA PERFORMANCE USING MACHINE LEARNING MODELS | PDF",1785901040,30,{"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},"enhancing-circular-microstrip-patch-antenna-performance-using-machine-learning-models","",{"@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/enhancing-circular-microstrip-patch-antenna-performance-using-machine-learning-models/125755/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this paper?","Question",{"text":75,"@type":76},"To design and optimize a circular microstrip patch antenna using machine learning models that can predict return loss (S11).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used to predict S11?",{"text":80,"@type":76},"Decision Tree, Random Forest, XG-Boost Regression, K-Nearest Neighbour (KNN), Gradient Boosting Regression (GBR), and Light Gradient Boosting Regression (LGBR).",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate is the best-performing model?",{"text":84,"@type":76},"KNN provides the highest accuracy, achieving 98.5% in predicting return loss (S11).","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]