[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125191-en":3,"doc-seo-125191-105":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":20,"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},125191,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","A Survey on the Applications of Machine Learning, Deep Learning, and Reinforcement Learning in Wireless Communications - comprehensive review","This survey examines how machine learning (ML), deep learning (DL), and reinforcement learning (RL) can be integrated into wireless communications. It reviews representative methods, algorithms, and practical applications while outlining key challenges and future research directions. The survey emphasizes the need for intelligent techniques to improve the performance and management of increasingly complex wireless networks under growing traffic and connected-device demands. Focus areas include network optimization, resource management, security, signal recognition, channel coding, traffic prediction, access control, and energy optimization, alongside emerging approaches such as federated learning, transfer learning, and multi-agent reinforcement learning for next-generation systems.","Review  \nA survey on the applications of machine learning, deep learning, and reinforcement learning in wireless communications  \nHuu Q. Tran1, Viet-Thanh Pham1, Sy Ngo2,*  \n1 Faculty of Electronics Technology (FET), Industrial University of Ho Chi Minh City (IUH), Ho Chi Minh City 70000, Vietnam  \n2 Institute of Engineering and Technology, Thu Dau Mot University (TDM), Thu Dau Mot City 75000, Vietnam  \n* Corresponding author: Sy Ngo, [syn@tdmu.edu.vn](syn@tdmu.edu.vn)  \nCITATION  \n\n| Tran HQ, Pham VT, Ngo S. A survey on the applications of machine learning, deep learning, and reinforcement learning in wireless communications. Computer and Telecommunication Engineering. 2025; 3(1): 3170. [https://doi.org/10.54517/cte3170](https://doi.org/10.54517/cte3170)\u003Cbr>ARTICLE INFO |\n| --- |\n| Received: 18 December 2024\u003Cbr>Accepted: 26 February 2025\u003Cbr>Available online: 20 March 2025\u003Cbr>COPYRIGHT |\n\nCopyright © 2025 by author(s) . Computer and Telecommunication Engineering is published by Asia Pacific Academy of Science Pte. Ltd. This work is licensed under the Creative Commons Attribution (CC BY) license. [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)[ ](https://creativecommons.org/licenses/)[by/4.0/](by/4.0/)  \nAbstract: This survey explores the integration of machine learning (ML), deep learning (DL), and reinforcement learning (RL) within wireless communications. It reviews various methods, algorithms, and applications while addressing the challenges and future research directions in this field. The paper highlights the necessity of intelligent techniques to enhance the performance and management of wireless networks, driven by the increasing complexity and demand for higher efficiency. Key areas of focus include network optimization, resource management, security, signal recognition, channel coding, traffic prediction, access control, and energy optimization. The survey also discusses emerging techniques such as federated learning, transfer learning, and multi-agent reinforcement learning, emphasizing their potential to revolutionize wireless communication systems.  \nKeywords: DL; edge intelligence; FL; IoT; MARL; ML; RL  \n1. Introduction  \nWireless communications have become a cornerstone of modern society, supporting an ever-expanding range of applications, from mobile communications and autonomous systems to the Internet of Things (IoT) [1], as illustrated in Figure 1. The journey of wireless communication began in the late 19th century with the theoretical prediction of electromagnetic waves by James Clerk Maxwell and their experimental validation by Heinrich Hertz [2] . The first practical wireless communication system was developed by Guglielmo Marconi, who successfully transmitted Morse code signals over long distances [2] . This laid the foundation for subsequent advancements, including the development of radio, television, and mobile communications. The evolution of wireless technologies has been marked by significant milestones, such asthe introduction of the first-generation (1G) mobile networks in the 1980s, which used analog signals, followed by the digital revolution with 2G, 3G, and 4G networks [3] . Each generation brought improvements in data rates, capacity, and reliability, culminating in the current deployment of 5G networks, which offer unprecedented speeds and low latency [3]. However, these advancements have also led to a significant surge in data traffic and the widespread adoption of connected devices [4] . Despite these advancements, traditional methods of network management and optimization are proving inadequate for the needs of contemporary wireless networks [5] . Conventional approaches often struggle with issues such as limited bandwidth, interference, and the increasing complexity of managing diverse and dynamic network environments [5,6] . These challenges necessitate the adoption of intelligent methods for optimization, resource allocation, and system adaptability [6]","cbCaio9Xka5PpB2n","https://ap.wps.com/l/cbCaio9Xka5PpB2n","pdf",422299,1,13,"English","en",105,"# Introduction\n## Background and motivation\n## Challenges in traditional wireless network management\n# Applications and techniques\n## Network optimization and resource management\n## Security, recognition, and prediction tasks\n## Emerging learning paradigms (FL, transfer learning, MARL)","[{\"question\":\"What is the central focus of the survey in wireless communications?\",\"answer\":\"The survey focuses on integrating machine learning (ML), deep learning (DL), and reinforcement learning (RL) into wireless communications and mapping their key applications and benefits.\"},{\"question\":\"Which wireless communication areas does the survey highlight for ML/DL/RL?\",\"answer\":\"It highlights network optimization, resource management, security, signal recognition, channel coding, traffic prediction, access control, and energy optimization.\"},{\"question\":\"What future directions or emerging techniques does the survey discuss?\",\"answer\":\"It discusses emerging directions such as federated learning, transfer learning, and multi-agent reinforcement learning, emphasizing their potential impact on future wireless systems.\"}]","A Survey on the Applications of Machine Learning, Deep Learning, and Reinforcement Learning in Wireless Communications - 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