2022 Japan challenge | AI/ML in 5G Challenge

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今回のウェビナーでは、日本で開催される2022 ITU AI/ML in 5Gチャレンジの以下2つのProblem Statementの紹介と、専門家による講演を実施します。

●KDDI – Network failure prediction on CNFs 5GC with Linux eBPF
LinuxeBPFを使用したCNF5GCでのネッ トワーク障害検知

(https://challenge.aiforgood.itu.int/match/matchitem/64)

このチャレンジでは、eBPFとcAdvisorによって得られた多変量時系列データから、正常時と障害時の各5GC CNFの基本メトリクスが提供されます。データからAI/MLを用いて、5GCにおけるネットワークの障害予測をすることが求められます。
今後ネットワーク障害予測を実現するためには、AI/ML技術の活用が必要とされます。参加者には、eBPFとcAdvisorから提供される数千の測定基準からなる時系列データを用いて、将来のネットワーク障害をいかに早く、正確に予測できるかに挑戦してもらいます。予測対象値は、5GCにおけるUE登録失敗数です。

●RISING – Location Estimation Using RSSI of Wireless LAN in NLoS Environment
NLoS環境におけるRSSIを用いた無線LANの位置推定

(https://challenge.aiforgood.itu.int/match/matchitem/65)

AI/MLを活用することで、データによる位置推定からモデルによる位置推定に代替できる可能性をこの課題では探ります。
RSS情報を用いたAI/MLによる位置推定技術の限界まで最終的に取り組みます。AI/MLによる位置推定は、GPSによる位置推定と同様の精度、あるいはそれ以上の精度を達成できるかについて追求します。

●専門家による無線LANにおける機械学習の応用
2人の専門家による機械学習および深層学習技術の無線通信ネットワークへの応用について解説します。

Speakers:
Akihiro Nakao, Professor, University of Tokyo
Koichi Adachi, Associate Professor, University of ElectroCommunications
Norihiro Fukumoto, Associate Professor, University of Tokyo
Takuya Miyasaka, Senior Manager, KDDI Research, Inc.
Takanori Iwai, Director, Secure System Platform Research Laboratories

Moderator:
Miya Nishio, Student, University of Tokyo

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