[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126103-en":3,"doc-seo-126103-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126103,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning-based spectrum occupancy prediction - a comprehensive survey","Machine learning-based spectrum occupancy prediction survey focuses on enabling efficient spectrum utilization in cognitive radio (CR) systems. It addresses limits of traditional statistical spectrum occupancy prediction methods under non-stationary conditions driven by user mobility and diversity in 6G and beyond networks. The work reviews problem definition, statistical baselines, and a detailed landscape of ML approaches, emphasizing deep learning techniques that exploit multidimensional correlations across time, frequency, and space. It further covers dataset generation, CR threats mitigation via ML detection, and future directions for ML-based spectrum occupancy prediction.","TYPE Review  \nPUBLISHED 22 January 2025  \nDOI 10.3389/frcmn.2025.1482698  \nOPEN ACCESS  \nEDITED BY  \nHong-Chuan Yang,  \nUniversity of Victoria, Canada  \nREVIEWED BY  \nQasim Zeeshan Ahmed,  \nUniversity of Huddersfield, United Kingdom Ahmad Bazzi,  \nNew York University Abu Dhabi, United Arab Emirates  \n*CORRESPONDENCE  \nMehmet Ali Aygül,  [aygul21@itu.edu.tr](aygul21@itu.edu.tr)  \nRECEIVED 18 August 2024  \nACCEPTED 06 January 2025  \nPUBLISHED 22 January 2025  \nCITATION  \nAygül MA, Çırpan HA and Arslan H (2025) Machine learning-based spectrum occupancy prediction: a comprehensive survey.  \nFront. Comms. Net 6:1482698 .  \ndoi: 10.3389/frcmn.2025.1482698  \nCOPYRIGHT  \n© 2025 Aygül, Çırpan and Arslan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based  \nspectrum occupancy prediction: a comprehensive survey  \nMehmet Ali Aygül 1,2*, Hakan Ali Ç ırpan1 and Hüseyin Arslan 3  \n1Department of Electronics and Communications Engineering, Istanbul Technical University, Istanbul, Türkiye, 2Department of Research and Development, Vestel, Manisa, Türkiye, 3Department of Electrical and Electronics Engineering, Istanbul Medipol University, Istanbul, Türkiye  \nIn cognitive radio (CR) systems, efficient spectrum utilization depends on the ability to predict spectrum opportunities. Traditional statistical methods for spectrum occupancy prediction (SOP) are insufficient for addressing the nonstationary nature of spectrum occupancy, especially with UEs’ increased mobility and diversity in the sixth-generation and beyond wireless networks. This survey provides a comprehensive overview of machine learning (ML)-based SOP methods that address these challenges. The paper begins with a brief discussion of problem definition and traditional statistical methods before delving into a detailed survey of ML-based methods. Various aspects of SOPare analyzed from a CR perspective, highlighting the multidimensional correlations in spectrum usage across time, frequency, space, etc. Key challenges and enabling methods for effective prediction are reviewed, focusing on deep learning methods that exploit these multidimensional correlations. The survey also covers dataset generation techniques for SOP. Additionally, the paper discusses CR threats that impair spectrum utilization and reviews ML methods for detecting these threats. The future directions for ML-based SOP are also given.  \nKEYWORDS  \n6G, cognitive radio, deep learning, machine learning, multi-dimensions, spectrum occupancy prediction  \n1 Introduction  \nAccommodating exploding data traffic is one of the most critical challenges for communication systems in the sixth generation (6G) and beyond (Zhang and Zhu, 2020; Guo et al., 2021) . In 6G networks, data rates are expected to exceed 1 terabit per second, and end-to-end delays will be reduced to less than 0.1 milliseconds. Additionally, 6G will provide access to powerful edge intelligence with processing delays below 10 nanoseconds and network reliability exceeding 99.99999% . The extreme connection density of over 10 million devices per square kilometer will support the Internet of everything (De Alwis et al., 2021) . Thus, there is an intrinsic gap with the limited spectrum available due to the ever-demanding nature of higher-rate communications (Amjad et al., 2018) .  \nOne potential solution to this gap is the recent integration of communication and sensing capabilities in 6G networks (Bazzi and Chafii, 2023; Chowdary et al., 2024), which aim to optimize the use of limited spectrum resources. However, this solution is limited to the integration o","cbCaiiq0nuhjSwHK","https://ap.wps.com/l/cbCaiiq0nuhjSwHK","pdf",2168951,5,1,21,"English","en",105,"# Introduction\n## Spectrum demand and 6G context\n## Cognitive radio and spectrum holes\n## Traditional spectrum occupancy prediction limits\n## Machine learning and deep learning approaches\n## Remaining challenges and open issues","[{\"question\":\"Why are traditional spectrum occupancy prediction methods insufficient for 6G and beyond networks?\",\"answer\":\"They often cannot handle the non-stationary nature of spectrum occupancy, which is increasingly shaped by user mobility and diversity. This reduces prediction reliability in modern wireless environments.\"},{\"question\":\"What ML approaches does the survey emphasize for spectrum occupancy prediction?\",\"answer\":\"It reviews methods from shallow neural networks to advanced deep learning, highlighting CNNs for spatial pattern extraction and LSTMs for temporal dependency modeling to improve prediction accuracy.\"},{\"question\":\"Which additional topics are covered beyond prediction models?\",\"answer\":\"The survey discusses dataset generation techniques, reviews ML methods for detecting CR threats that impair spectrum utilization, and outlines future directions for ML-based SOP.\"}]","Machine learning-based spectrum occupancy prediction - a comprehensive survey | PDF",1785903140,53,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-spectrum-occupancy-prediction-a-comprehensive-survey","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-spectrum-occupancy-prediction-a-comprehensive-survey/126103/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why are traditional spectrum occupancy prediction methods insufficient for 6G and beyond networks?","Question",{"text":77,"@type":78},"They often cannot handle the non-stationary nature of spectrum occupancy, which is increasingly shaped by user mobility and diversity. This reduces prediction reliability in modern wireless environments.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What ML approaches does the survey emphasize for spectrum occupancy prediction?",{"text":82,"@type":78},"It reviews methods from shallow neural networks to advanced deep learning, highlighting CNNs for spatial pattern extraction and LSTMs for temporal dependency modeling to improve prediction accuracy.",{"name":84,"@type":75,"acceptedAnswer":85},"Which additional topics are covered beyond prediction models?",{"text":86,"@type":78},"The survey discusses dataset generation techniques, reviews ML methods for detecting CR threats that impair spectrum utilization, and outlines future directions for ML-based SOP.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]