ITU AI/ML in 5G Challenge —”Machine Learning for Wireless LANs + Japan Challenge Introduction”
In this webinar, we will host expert talks and introduce ITU-ML5G-PS-031: Network State Estimation by Analyzing Raw Video Data and ITU-ML5G-PS-032: Analysis on route information failure in IP core networks by NFV-based test environment.
This first part of the webinar will be on Machine Learning for Wireless LANs. This talk provides applications of deep supervised learning and reinforcement learning for microwave and mmWave wireless LANs (WLANs). Furthermore, the talk will address how to apply the machine learning techniques to challenges in WLANs based on mmWave, received power prediction and handover.
Second part will be introducing problem statements from Japan Challenge. Firstly, “Analysis on route information failure in IP core networks by NFV-based test environment” will be discussed. The stable and high-quality Internet connectivity is mandatory to 5G mobile networks. Anomaly detection is desired to be automatically and rapidly performed by AI/ML. In this problem, the data sets at border gateway routers are provided along with network status information such as normal, failure, mis-operation, etc, as normal/abnormal labels. Participants are required to create the model to pinpoint the network status of failures and mis-operation using the data sets and evaluate the performance of the developed model.
Secondly, Network State Estimation by Analyzing Raw Video Data will be discussed. Due to COVID-19 pandemic, the importance of interactive live video streaming services, e.g., telework system using web cameras, has been increasing. However, the increasing traffic generated from such bandwidth-consuming video streaming services results in heavy congestion. Service providers should control the video quality according to the network state. This situation is challenging for passive network state estimation by analyzing raw video data. This challenge is the first step to understand relationship between raw video images and network state. The goal of this challenge is to estimate network state, i.e., throughput and loss ratio, from given raw video data sets. The participants are expected to train and test an AI model using the video data with labels of network state.
Speakers, Panelists and Moderators
AKIHIRO NAKAOProfessorUniversity of TokyoDr. Aki Nakao is a professor in Applied Computer Science at the University of Tokyo. He received Ph.D. degree in Computer Science from Princeton University. He has been teaching at the University of Tokyo since 2005, leading research group at Nakao Research Laboratory pursuing networked systems. His main study areas include, but are not limited to, SDN, NFV, Network Virtualization, In-Network Processing for Smartphones, Wearables and Cloud interactions, etc.
KOJI YAMAMOTOAssociate ProfessorKyoto UniversityKoji Yamamoto received the Ph.D. degree in informatics from Kyoto University in 2005. He is currently an Associate Professor in communications and computer engineering with the Graduate School of Informatics, Kyoto University. His research interests include radio resource management, game theory, and machine learning. He was a tutorial lecturer in IEEE ICC 2019.
TOMOHIRO OTANIExecutive DirectorKDDI Research, Inc.Tomohiro Otani is an executive director of KDDI Research, Inc. and responsible for R&D activities of beyond 5G networking technologies, operation automation and IoT. Prior to that, he was a general manager of Operation Support System Development Department of Operations Sector in KDDI Corporation and responsible for developing the operation supporting systems (OSS) for fixed and mobile networks.
TAKANORI IWAIResearch ManagerNEC CorporationTakanori Iwai received his B.E. and M.E. degrees in electrical and electronic engineering from Shinshu University, Japan, in 2002 and 2004. In 2004, he joined NEC Corporation, Japan, and is a Research Manager at System Platform Research Laboratories. His research interests include system control, mobile and wireless networking, and IoT service networks. He received the Electrical Science and Engineering Award from them the Promotion Foundation for Electrical Science and Engineering in 2016, and the Best Paper Award from the IEEE ComSoc International Communications Quality and Reliability Workshop (CQR’17).