2022 Japan challenge (network failure prediction & location estimation) | AI/ML IN 5G CHALLENGE
0This webinar, will introduce 2 problem statements for the 2022 ITU AI/ML in 5G challenge hosted in Japan as well as expert talks;\n\nML5G-PS-005: Network failure prediction on CNFs 5GC with Linux eBPF \nhttps://challenge.aiforgood.itu.int/match/matchitem/64 \n\nIn this challenge, participants will be asked to predict future network failures on the 5GC using AI/ML. This challenge will provide multi-variant time-series data obtained by eBPF and cAdvisor that provides basic metrics for each 5GC CNF under normal and failure conditions. To realize network failure prediction, AI/ML technology is expected to be leveraged. Participants are asked to challenge how early and accurately the future network failures can be predicted using time-series data consisting of thousands of metrics provided by eBPF and cAdvisor. The target value for prediction is the number of UE registration failures in the 5GC. \n\nML5G-PS-006: Location Estimation Using RSSI of Wireless LAN in NLoS Environment https://challenge.aiforgood.itu.int/match/matchitem/65\n\nThis challenge explores the possibility that the data-oriented localization technique can replace the model-based localization with the help of powerful AI/ML techniques. Ultimately, this challenge tries to tackle the limitation of AI/ML-based localization using RSS information; Can AI/ML-based localization technique achieve similar accuracy as the GPS-based location technique or even better accuracy? \n\n2 Expert talks: Machine Learning for Wireless LANs \n\nThe two expert talk provides applications of machine and deep learning techniques to wireless communication networks.\n\n Speakers:\nAkihiro Nakao, Professor, University of Tokyo\nKoichi Adachi, Associate Professor, University of ElectroCommunications\nAnan Sawabe, Researcher, NEC Corporation\nTakuya Miyasaka, Senior Manager, KDDI Research, Inc.\n\n Moderators:\nMiya Nishio, Student, University of Tokyo\nNorihiro Fukumoto, Associate Professor, University of Tokyo\n\n#ML5G #FutureNetworks\n\nJoin the Neural Network! \nhttps://aiforgood.itu.int/neural-network/\nThe AI for Good networking community platform powered by AI. \nDesigned to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\n\n Watch the latest #AIforGood videos!\n\n\nExplore more #AIforGood content:\n AI for Good Top Hits\n • Top Hits \n\n AI for Good Webinars\n • AI for Good Webinars \n\n AI for Good Keynotes\n • AI for Good Keynotes \n\n Stay updated and join our weekly AI for Good newsletter:\nhttp://eepurl.com/gI2kJ5\n\n Discover what's next on our programme!\nhttps://aiforgood.itu.int/programme/\n\nCheck out the latest AI for Good news:\nhttps://aiforgood.itu.int/newsroom/\n\nExplore the AI for Good blog:\nhttps://aiforgood.itu.int/ai-for-good-blog/\n\n Connect on our social media:\nWebsite: https://aiforgood.itu.int/\nTwitter: https://twitter.com/ITU_AIForGood\nLinkedIn Page: https://www.linkedin.com/company/26511907 \nLinkedIn Group: https://www.linkedin.com/groups/8567748 \nInstagram: https://www.instagram.com/aiforgood \nFacebook: https://www.facebook.com/AIforGood\n\nWhat is AI for Good?\nThe AI for Good series is the leading action-oriented, global & inclusive United Nations platform on AI. The Summit is organized all year, always online, in Geneva by the ITU with XPRIZE Foundation in partnership with over 35 sister United Nations agencies, Switzerland and ACM. The goal is to identify practical applications of AI and scale those solutions for global impact.\n\nDisclaimer:\nThe views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.