HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks

Linh Truong, Nguyen Ngoc Nhu Trang

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

198 Lataukset (Pure)

Abstrakti

This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.
AlkuperäiskieliEnglanti
Otsikko2022 IEEE International Conference on Big Data (IEEE BigData 2022)
ToimittajatShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
KustantajaIEEE
Sivut2389-2393
Sivumäärä5
ISBN (elektroninen)978-1-6654-8045-1
ISBN (painettu)978-1-6654-8046-8
DOI - pysyväislinkit
TilaJulkaistu - 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Big Data - Osaka, Japani
Kesto: 17 jouluk. 202220 jouluk. 2022

Conference

ConferenceIEEE International Conference on Big Data
LyhennettäBigData
Maa/AlueJapani
KaupunkiOsaka
Ajanjakso17/12/202220/12/2022

Sormenjälki

Sukella tutkimusaiheisiin 'HAIVAN: a Holistic ML Analytics Infrastructure for a Variety of Radio Access Networks'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä