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

Author

Listed:
  • Jiantao Qu

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

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.

Suggested Citation

  • Jiantao Qu & Chunyu Qi & He Meng, 2024. "An Imbalanced Sequence Feature Extraction Approach for the Detection of LTE-R Cells with Degraded Communication Performance," Future Internet, MDPI, vol. 16(1), pages 1-20, January.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:1:p:30-:d:1320332
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