IEICE TRANSACTIONS on Communications
Analysis and Identification of Root Cause of 5G Radio Quality Deterioration Using Machine Learning
Summary :
It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.12 pp.1286-1292
- Publication Date
- 2023/12/01
- Publicized
- 2023/06/02
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022TMT0002
- Type of Manuscript
- Special Section PAPER (Special Section on Towards Management for Future Communications and Services in Conjunction with Main Topics of APNOMS2022)
- Category
Authors
Yoshiaki NISHIKAWA
NEC
Shohei MARUYAMA
NEC
Takeo ONISHI
NEC
Eiji TAKAHASHI
NEC
Keyword
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Yoshiaki NISHIKAWA, Shohei MARUYAMA, Takeo ONISHI, Eiji TAKAHASHI, "Analysis and Identification of Root Cause of 5G Radio Quality Deterioration Using Machine Learning" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 12, pp. 1286-1292, December 2023, doi: 10.1587/transcom.2022TMT0002.
Abstract: It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022TMT0002/_p
Copy
@ARTICLE{e106-b_12_1286,
author={Yoshiaki NISHIKAWA, Shohei MARUYAMA, Takeo ONISHI, Eiji TAKAHASHI, },
journal={IEICE TRANSACTIONS on Communications},
title={Analysis and Identification of Root Cause of 5G Radio Quality Deterioration Using Machine Learning},
year={2023},
volume={E106-B},
number={12},
pages={1286-1292},
abstract={It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.},
keywords={},
doi={10.1587/transcom.2022TMT0002},
ISSN={1745-1345},
month={December},}
Copy
TY - JOUR
TI - Analysis and Identification of Root Cause of 5G Radio Quality Deterioration Using Machine Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 1286
EP - 1292
AU - Yoshiaki NISHIKAWA
AU - Shohei MARUYAMA
AU - Takeo ONISHI
AU - Eiji TAKAHASHI
PY - 2023
DO - 10.1587/transcom.2022TMT0002
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2023
AB - It has become increasingly important for industries to promote digital transformation by utilizing 5G and industrial internet of things (IIoT) to improve productivity. To protect IIoT application performance (work speed, productivity, etc.), it is often necessary to satisfy quality of service (QoS) requirements precisely. For this purpose, there is an increasing need to automatically identify the root causes of radio-quality deterioration in order to take prompt measures when the QoS deteriorates. In this paper, a method for identifying the root cause of 5G radio-quality deterioration is proposed that uses machine learning. This Random Forest based method detects the root cause, such as distance attenuation, shielding, fading, or their combination, by analyzing the coefficients of a quadratic polynomial approximation in addition to the mean values of time-series data of radio quality indicators. The detection accuracy of the proposed method was evaluated in a simulation using the MATLAB 5G Toolbox. The detection accuracy of the proposed method was found to be 98.30% when any of the root causes occurs independently, and 83.13% when the multiple root causes occur simultaneously. The proposed method was compared with deep-learning methods, including bidirectional long short-term memory (bidirectional-LSTM) or one-dimensional convolutional neural network (1D-CNN), that directly analyze the time-series data of the radio quality, and the proposed method was found to be more accurate than those methods.
ER -
English
