TY - GEN
T1 - Novel UE RF condition estimation algorithm by integrating machine learning
AU - Dong, Yupu
AU - Pan, Zhenni
AU - Ernawan, Mohamad Erick
AU - Liu, Jiang
AU - Shimamoto, Shigeru
AU - Wicaksono, Ragil Putro
AU - Kunishige, Seiji
AU - Chang, Kwangrok
N1 - Funding Information:
This paper is sponsored by China Scholarship Council.
Publisher Copyright:
© Springer Science+Business Media Singapore 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Estimation
KW - Machine learning
KW - Mobility problem
KW - RF condition
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U2 - 10.1007/978-981-10-5281-1_12
DO - 10.1007/978-981-10-5281-1_12
M3 - Conference contribution
AN - SCOPUS:85022181162
SN - 9789811052804
T3 - Lecture Notes in Electrical Engineering
SP - 102
EP - 113
BT - Mobile and Wireless Technologies 2017 - ICMWT 2017
A2 - Joukov, Nikolai
A2 - Kim, Kuinam J.
PB - Springer Verlag
T2 - 4th iCatse Conference on Mobile and Wireless Technology, ICMWT 2017
Y2 - 26 June 2017 through 29 June 2017
ER -
