[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118331-en":3,"doc-seo-118331-105":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},118331,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Based Beam Selection for Maximizing Wireless Network Capacity","Machine learning is pivotal for today’s and future wireless communications, particularly in 5G and 6G networks, where it supports higher throughput, better security, lower latency, and improved overall network efficiency. This work studies reinforcement learning to predict optimal beam configurations by minimizing interference through selecting optimal beamforming angles. Ray tracing is used to model the propagation scenario, enabling comparison of multiple ML methods and evaluation of performance and accuracy. The study extends to cases with more transmitters and receivers and investigates optimal antenna angles for higher channel capacity. It also frames the results as a step toward integrating digital twin technology into network management and control.","SPECIAL SECTION ON INNOVATIVE TRENDS IN 6G ECOSYSTEMS  \nReceived 21 February 2024, accepted 14 March 2024, date of publication 25 March 2024, date of current version 29 March 2024. Digital Object Identifier 10.1109/ACCESS.2024.3381542  \nMachine Learning Based Beam Selection for Maximizing Wireless Network Capacity  \nPARMIDA GERANMAYEH1 AND ECKHARD GRASS1,2  \n1Computer Science Department, Humboldt University of Berlin, 10117 Berlin, Germany  \n2IHP—Leibniz-Institut für innovative Mikroelektronik, 15236 Frankfurt (oder), Germany Corresponding author: Parmida Geranmayeh ([Parmida.Geranmayeh@hu-berlin.de](Parmida.Geranmayeh@hu-berlin.de))  \nThis work was supported by the Deutsche Forschungsgemeinschaft (DFG) funded by the Research Project titled 5GEnabled Real Time Communications for ‘‘Tactile Internet’’ (5G-Remote) under Project 457407152 .  \nABSTRACT In today’s and future wireless communications, especially in 5G and 6G networks, machine learning (ML) methods are crucial. Potentially, these techniques bring many benefits such as increased data throughput, improved security, reduced latency, and, on the whole, enhanced network efficiency. Furthermore, to facilitate the processing of large amounts of data in real-time situations, machine learning is used for various functions in wireless networks. This article aims to explore the significance and application of machine learning, with a particular focus on classic reinforcement learning, in the context of predicting optimal beam configurations within wireless communications scenarios. Our goal is to minimize interference between transmitters by finding the optimal beamforming angles. For this, ray tracing techniques are deployed. We see this research as a step forward towards integrating digital twin (DT) technology in network management and control. In this article, different machine learning methods are used and their performance is compared. Firstly, the most effective angles for beamforming, maximizing channel capacity are identified. Then, by using these methods and after verifying their accuracy, the optimal antenna angles in scenarios with an increased number of transmitters and receivers is found and evaluated.  \nINDEX TERMS Beamforming, machine learning, network capacity, ray-tracing.  \nI. INTRODUCTION  \nFor reaching the ambitious goals of next generation networks, more optimal utilization of the limited resource bandwidth by spatial re-use and multiplexing is needed. To meet the evolving demands of future networks, 6G is slated to embrace an extensive integration of artificial intelligence (AI) and machine learning (ML) techniques [1] . This deployment of AI and ML is aimed at achieving heightened automation and superior operational reliability, currently unattainable by the incumbent 5G technology [2] . Many research endeavors are already underway to lay the foundations for 6G wireless communication networks.  \nMachine learning, which mimics human cognitive processes, is critical for improving wireless communication in various ways. It enhances computer vision, image processing,  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Zaharias D. Zaharis .  \nparallel processing, distributed processing, analytics, and prediction capabilities [3] . In machine learning, models are extensively trained using datasets to ensure they perform well across a variety of examples. This is particularly crucial fortasks such as image classification and sentiment analysis [4] . The use of machine learning in controlling communication systems has seen significant growth recently [5], [6],[7], [8], [9] . Key references cover a range of applications, including source and channel coding [10],[11],[12], waveform design [13], signal detection [14], [15], [16], resource allocation [17],[18],[19],[20],[21],[22], and channel estimation [23], [24], among others. This expanding field, [5] highlights the effectiveness of a machine-learning approach in a","cbCainvUxvIN7Fop","https://ap.wps.com/l/cbCainvUxvIN7Fop","pdf",1099226,1,11,"English","en",105,"# Introduction\n## Machine learning and AI integration in 6G\n## Reinforcement learning for beam configuration prediction\n# Methodology and modeling\n## Ray tracing for wireless scenarios\n# Results and evaluation\n## ML method comparison for capacity maximization\n## Scaling to more transmitters and receivers","[{\"question\":\"What problem does the paper address in wireless networks?\",\"answer\":\"The paper targets how to predict and select optimal beamforming angles so that channel capacity is maximized while interference between transmitters is minimized.\"},{\"question\":\"Which techniques are used to determine the beam configurations?\",\"answer\":\"Ray tracing is deployed to model the wireless propagation, and multiple machine learning methods—especially reinforcement learning—are used to find optimal beam and antenna angles.\"},{\"question\":\"How does the study evaluate the ML approaches?\",\"answer\":\"The work compares different ML methods based on their accuracy in identifying effective beamforming angles, then evaluates performance in scenarios with increased numbers of transmitters and receivers.\"}]","Machine Learning Based Beam Selection for Maximizing Wireless Network Capacity | 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problem does the paper address in wireless networks?","Question",{"text":75,"@type":76},"The paper targets how to predict and select optimal beamforming angles so that channel capacity is maximized while interference between transmitters is minimized.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which techniques are used to determine the beam configurations?",{"text":80,"@type":76},"Ray tracing is deployed to model the wireless propagation, and multiple machine learning methods—especially reinforcement learning—are used to find optimal beam and antenna angles.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study evaluate the ML approaches?",{"text":84,"@type":76},"The work compares different ML methods based on their accuracy in identifying effective beamforming angles, then evaluates performance in scenarios with increased numbers of transmitters and 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