[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122365-en":3,"doc-seo-122365-105":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},122365,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Comprehensive Survey of Machine Learning Applied to Resource Allocation in Wireless Communications","The survey examines how artificial intelligence and machine learning advance resource allocation in wireless communications, especially within modern networks such as 5G and emerging 6G. It explains data-driven use of machine learning to infer customer behavior, network performance, and market trends, and connects these insights to optimization goals including reduced latency, congestion, and downtime. The work also outlines how intelligent resource management increases network capacity, improves user experience, supports IoT integration, and can reduce energy consumption for a more sustainable network future.","This article has been accepted for publication in IEEE Communications Surveys & Tutorials. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10. 1109/COMST.2025.3552370  \nJOURNAL OF IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. X, NO. Y, FEBRUARY 2024 1  \nA Comprehensive Survey of Machine Learning Applied to Resource Allocation in Wireless  \nCommunications  \nDiego Gabriel Soares Pivoto, Felipe A. P. de Figueiredo, Cicek Cavdar, Member, IEEE, Gustavo Rodrigues de  \nLima Tejerina, and Luciano Leonel Mendes, Member, IEEE  \nAbstract—Telecommunications play a pivotal role in shaping today’s interconnected world by fostering global development, supporting seamless information exchange across vast distances, and revolutionizing the interactions between individuals, businesses, and governments. Accessible and reliable communication networks transcend geographical barriers, promoting economic growth, the dissemination of knowledge, and societal connectivity. The integration of artificial intelligence into telecommunications has been transformative, revolutionizing the entire industry by improving system efficiency, allowing new services, and reducing complexity. By leveraging machine learning algorithms, telecommunication operators analyze vast data sets to gain insights into customer behavior, network performance, and market trends. This data-driven approach enhances service efficiency, leading to optimized network deployment, improved customer experience, and targeted marketing strategies. Machine learning’s impact extends to resource allocation optimization. Intelligent management of network resources reduces latency, congestion, and downtime, ensuring enhanced user experiences and increased overall network capacity. This optimization is vital for integrating emerging technologies like the Internet of Things and future generations of mobile systems and promoting sustainability by reducing energy consumption, contributing to a greener future. As technology evolves, the synergy between telecommunications and artificial intelligence will pave the way for a more connected, intelligent, and prosperous future. Given the relevance of this research topic, this paper presents a comprehensive survey of machine learning techniques applied to resource allocation in wireless communication systems. The objective is to guide the  \nManuscript received February 27, 2024; revised Month Day, 2024 .  \nThis work was partially funded by CNPq (Grant Nos. 403612/2020- 9, 311470/2021-1, 303282/2021-5 and 403827/2021-3), by Minas Gerais Research Foundation (FAPEMIG) (Grant Nos. PPE-00124-23, APQ-00810- 21, APQ-04523-23, APQ-05305-23, and APQ-03162-24), by the projects XGM-AFCCT-2024-2-5-1, and XGM-AFCCT-2024-9-1-1 supported by xGMobile - EMBRAPII-Inatel Competence Center on 5G and 6G Networks, with financial resources from the PPI IoT/Manufatura 4.0 from MCTI grant number 052/2023, signed with EMBRAPII, and by the Research Council of Finland (former Academy of Finland) 6G Flagship Programme (Grant Number: 346208) . The authors also acknowledge the support of CAPES through the Transformative Agreement with IEEE, which enabled the openaccess publication of this article.  \nD. G. S Pivoto, and L. L. Mendes are with the Radiocommunications Reference Center, National Institute of Telecommunications, Santa Rita do Sapuca´ı, MG, 37540000, Brazil (e-mail: [diegopivoto@gea.inatel.br](diegopivoto@gea.inatel.br); lu[cianol@inatel.br](cianol@inatel.br)) .  \nF. A. P. de Figueiredo is with the Wireless and Artificial Intelligence Laboratory, National Institute of Telecommunications, Santa Rita do Sapuca MG, 37540000, Brazil ([e-mail: felipe.figueiredo@inatel.br](e-mail: felipe.figueiredo@inatel.br)).  \nC. Cavdar is with the Department of Computer Science, KTH Royal Institute of Technology, Kista, 164 40 Stockholm, Sweden (e-mail: cav[dar@kth.se](dar@kth.se)) .  \n[G. R. de](G. R. de) L. 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performance?\",\"answer\":\"It helps increase overall network capacity and improves user experience, while also supporting sustainable operation by reducing energy consumption as IoT and next-generation mobile systems expand.\"}]","A Comprehensive Survey of Machine Learning Applied to Resource Allocation in Wireless Communications | 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