An Imbalanced Sequence Feature Extraction Approach for the Detection of LTE-R Cells with Degraded Communication Performance

Jiantao Qu (), Chunyu Qi and He Meng
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Jiantao Qu: School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Chunyu Qi: National Engineering Research Center for Digital Construction and Evaluation Technology of Urban Rail Transit, China Railway Design Corporation, Co., Ltd., Tianjin 300308, China
He Meng: National Engineering Research Center for Digital Construction and Evaluation Technology of Urban Rail Transit, China Railway Design Corporation, Co., Ltd., Tianjin 300308, China

Future Internet, 2024, vol. 16, issue 1, 1-20

Abstract: Within the Shuo Huang Railway Company (Suning, China ) the long-term evolution for railways (LTE-R) network carries core wireless communication services for trains. The communication performance of LTE-R cells directly affects the operational safety of the trains. Therefore, this paper proposes a novel detection method for LTE-R cells with degraded communication performance. Considering that the number of LTE-R cells with degraded communication performance and that of normal cells are extremely imbalanced and that the communication performance indicator data for each cell are sequence data, we propose a feature extraction neural network structure for imbalanced sequences, based on shapelet transformation and a convolutional neural network (CNN). Then, to train the network, we set the optimization objective based on the Fisher criterion. Finally, using a two-stage training method, we obtain a neural network model that can distinguish LTE-R cells with degraded communication performance from normal cells at the feature level. Experiments on a real-world dataset show that the proposed method can realize the accurate detection of LTE-R cells with degraded communication performance and has high practical application value.

Keywords: LTE-R; abnormal detection; shapelet transformation; imbalanced data classification; feature extraction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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