The Study of Application of Multi-label Classification Methods to Type Selection of Mobile Base-Station

Shi, Wangyang; Wu, Lei

doi:10.1109/iccis.2012.352

Cited by 1 publication

(1 citation statement)

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“…Here comes the need for multi-label classi ication as for this problem statement, the values would be related to more than one label, with the labels depending on each other as well. Shi and Wu [6] describe preliminary research to construct forecasting models of type selection on the basis of historical data of mobile base stations. Bi and Kwok [7] propose a randomized-sampling based approach to classify examples into multiple labels.…”

Section: Related Workmentioning

confidence: 99%

Decision tree-based radio link failure prediction for 5G communication reliability

Sivakumar¹,

Srinivasan²,

Viswanath³

et al. 2022

ITU J-FET

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Stable and high-quality Internet connectivity is mandatory for 5G mobile networks. Network disruption may occur due to unexpected variations in environmental conditions such as weather, wind, and natural or man-made surroundings, and the influence of the defect is quite severe. Prediction of such undesirable events at a low cost can boost 5G communication reliability, massive network capacity, and decreased latency. This research work makes use of novel preprocessing and feature engineering techniques, followed by a trained decision tree model to predict the occurrence of Radio Link Failure (RLF). This system is designed to predict RLF for not just the next day, but also any of the next 5 days. This prediction supports reliance and increasing demand for good Internet connectivity. In order to achieve accurate RLF prediction, comprehensive data has been used which undergoes preprocessing. To account for the influence of surrounding weather conditions on radio links, the proposed system makes use of information from the past i.e., previous RLFs, and the information from the future i.e., the weather forecast from the weather station around the radio link station. The decision tree model was trained with the integration of feature engineering. A macro-averaged F1-score of 70% and 77% were obtained for RLF prediction for the next day and RLF prediction for the next 5 days, respectively. The results show improvement in performance after the incorporation of feature engineering in the pipeline. Further, an additional metric termed G-Mean is introduced in the paper. Owing to the high imbalance in the dataset, this metric was found to provide a more realistic representation of the results. The G-Mean score was found to be 98.69% and 92.89% for RLF prediction for the next day and RLF prediction for the next 5 days, respectively.

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Section: Related Workmentioning

confidence: 99%