Novel UE RF condition estimation algorithm by integrating machine learning

Yupu Dong, Zhenni Pan, Mohamad Erick Ernawan, Jiang Liu, Shigeru Shimamoto, Ragil Putro Wicaksono, Seiji Kunishige, Kwangrok Chang

研究成果: Conference contribution

抄録

By 2020, 5G era will be commercially available. The smart city construction will also make great progress. Compared to current situation, more than thousand times of devices will connect to the cellular networks. For the operators, in order to analyze overall network performance, it is a key factor to estimate the user equipment (UE) radio frequency (RF) condition. However, practical RF estimation scheme is based on UE data log which can only observe UE that is at the top-serving cell with good RF condition. However, according to the comparison of actual UE data log and the scanner data log, potential RF problems may still exist since the UE will not always be served by the top-1 cell. In this paper, we propose a novel estimation scheme by integrating machine learning (ML) algorithm to analyze the scanner data logs from the target estimation zones where the mobility problems may occur. A hypothesis is obtained from learning step by various kinds of RF condition as input features. The numerical results show that the proposed estimation algorithm integrated ML can estimate probability of the potential mobility problems accurately.

本文言語English
ホスト出版物のタイトルMobile and Wireless Technologies 2017 - ICMWT 2017
編集者Nikolai Joukov, Kuinam J. Kim
出版社Springer Verlag
ページ102-113
ページ数12
ISBN(印刷版)9789811052804
DOI
出版ステータスPublished - 2018
イベント4th iCatse Conference on Mobile and Wireless Technology, ICMWT 2017 - Kuala Lumpur, Malaysia
継続期間: 2017 6 262017 6 29

出版物シリーズ

名前Lecture Notes in Electrical Engineering
425
ISSN(印刷版)1876-1100
ISSN(電子版)1876-1119

Other

Other4th iCatse Conference on Mobile and Wireless Technology, ICMWT 2017
CountryMalaysia
CityKuala Lumpur
Period17/6/2617/6/29

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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