[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117156-en":3,"doc-seo-117156-105":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},117156,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",6,"Technology","Network Slicing for Beyond 5G Networks using Machine Learning - paper","This paper explores practical applications of machine learning in Beyond 5G (B5G) communications, focusing on network slicing optimization. Multiple machine learning techniques, including neural networks, are applied to a labeled dataset for virtual slice control. Results indicate strong performance in improving efficiency and adaptability of network resource allocation, supporting reliable Internet of Things (IoT) connectivity. The work provides actionable perspectives for telecommunications professionals and policymakers on applying AI in large-scale B5G networks.","Network Slicing for Beyond 5G Networks using  \nMachine Learning  \nEleni Aloupogianni Interactive Coventry Ltd  \nCoventry, UK [eleni@interactivecoventry.com](eleni@interactivecoventry.com)  \nRahat Iqbal Interactive Coventry Ltd  \nCoventry, UK[r.iqbal@interactivecoventry.com](r.iqbal@interactivecoventry.com)  \nCharalampos Karyotis  \nInteractive Coventry Ltd Coventry, UK  \n[charalampos.karyotis@interactivecoventry.com](charalampos.karyotis@interactivecoventry.com)  \nTomasz Maniak  \nInteractive Coventry Ltd Coventry, UK  \n[tomasz.maniak@interactivecoventry.com](tomasz.maniak@interactivecoventry.com)  \nNikos Passas  \nUniversity of Athens Athens, Greece [passas@di.uoa.gr](passas@di.uoa.gr)  \nZoran Vujicic  \nInstituto de Telecomunicacoes Aveiro, Portugal [zvujicic@av.it.pt](zvujicic@av.it.pt)  \nFaiyaz Doctor  \nUniversity of Essex Colchester, UK [fdocto@essex.ac.uk](fdocto@essex.ac.uk)  \nAbstract—This paper explores the application of machine learning for practical applications in the context of Beyond 5G (B5G) communications. A variety of machine learning techniques, including neural networks, was applied on a labeled dataset about network slicing. Neural network models demonstrate superior performance in optimizing virtual network slices, crucial for enhancing Internet of Things (IoT) connectivity and efficiency. The findings can assist telecommunications professionals and policymakers, offering practical perspectives on AI technologies that can be applied in B5G scenarios for large communications networks.  \nIndex Terms—neural networks, network slicing, 6G communications, IoT communications, beyond 5G  \nI. INTRODUCTION  \nThis paper investigates possible applications of Artificial Intelligence (AI) for resource management procedures for Beyond 5G (B5G) networks. It highlights the complex nature of B5G networks and demonstrates how AI-driven solutions, leveraging neural network techniques, enhance efficiency and adaptability in resource allocation. Additionally, the study emphasizes the critical role of intelligent resource management in femtocell-based communications and Cloud-RAN environments. Big data applications assume larger throughput and speeds from the transmission network [Iqbal et al., 2020a],[Iqbal et al., 2020b] . Network slicing has the potential to customize network capabilities for specific requirements, particularly in the context of highly-connected networks and Internet of Things (IoT) applications. This automation is a crucial factor in enhancing network capacity and coverage, particularly benefiting IoT devices with constant connectivity and high data throughput needs, such as those used in smart cities and health monitoring systems.  \nThe upcoming sixth-generation wireless technology, 6G [Latva-aho and Leppnen, 2019], is currently under development, aiming to succeed 5G. Expected characteristics include ultra-wideband and ultra-low latency communication with a target of one microsecond latency, significantly higher data rates, improved network reliability and accuracy, a pivotal role for AI in infrastructure and optimization, a human-centric  \nfocus with enhanced security and privacy features [Wahid et al., 2018], energy efficiency considerations [Qureshi et al., 2017], support for diverse applications beyond current mobile use scenarios (such as Internet of Things), and the adoption of flexible decentralized business models by mobile network operators [Letaief et al., 2019], [Docomo, 2020], [Tataria et al., 2021], [Alsharif et al., 2020] . The first 6G Wireless Summit was held in Levi, Finland in 2019 [University of Oulu, 2019] and the deployment of 6G systems is expected by 2028, although universally accepted standards defining its components do not yet exist.  \nAI is poised to play a pivotal role in the evolution of 6G technology, offering a range of transformative applications [Shi et al., 2023], [Yang et al., 2020], [Guo, 2020] . AI and communications convergence is expected to bridge the gap between digita","cbCaiiyJc625gZ5X","https://ap.wps.com/l/cbCaiiyJc625gZ5X","pdf",194923,1,4,"English","en",105,"# Introduction\n## Beyond 5G resource management with AI\n## 6G expectations and AI roles\n## Prior AI approaches for B5G networks","[{\"question\":\"What problem does the paper address in Beyond 5G networks?\",\"answer\":\"It examines how artificial intelligence can support resource management procedures in complex Beyond 5G (B5G) networks, improving efficiency and adaptability in allocation.\"},{\"question\":\"How is machine learning used in network slicing in this work?\",\"answer\":\"The study applies machine learning techniques, including neural networks, to a labeled dataset to optimize virtual network slices and control resources for different requirements.\"},{\"question\":\"Why are network slicing and AI important for IoT and future 6G scenarios?\",\"answer\":\"Network slicing can tailor network capabilities for highly connected and IoT use cases, while AI-driven automation helps networks manage resources effectively, supporting constant connectivity and high throughput needs.\"}]","Network Slicing for Beyond 5G Networks using Machine Learning - 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