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A study on the prediction of service reliability of wireless telecommunication system via distribution regression

Author

Listed:
  • Li, Yan-Fu
  • Zhao, Wei
  • Zhang, Chen
  • Ye, Jiantao
  • He, Huiru

Abstract

The reliability of wireless telecommunication service has become a major concern for the operation and maintenance (O&M) departments of the major telecommunication service providers. Consequently, reliability prediction has assumed a pivotal role for O&M department to furnish dependable services while simultaneously curtailing operational costs. However, predicting the service reliability is a challenging issue mainly attribute to the mobility characteristics of the end-users in the network. In tandem with the formulation of service reliability metrics, this study introduces a linear distribution regression model under Gaussian distribution and a kernel-based distribution regression model tailored for bimodal scenarios. The empirical validation of these proposed metrics and methodologies is substantiated through case studies employing both simulated data and real-world datasets derived from a wireless telecommunication system within an urban district. The outcomes of these case studies demonstrate the efficacy and applicability of the proposed metrics and methods.

Suggested Citation

  • Li, Yan-Fu & Zhao, Wei & Zhang, Chen & Ye, Jiantao & He, Huiru, 2024. "A study on the prediction of service reliability of wireless telecommunication system via distribution regression," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
  • Handle: RePEc:eee:reensy:v:250:y:2024:i:c:s0951832024003636
    DOI: 10.1016/j.ress.2024.110291
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    1. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    2. Lee, Dongjin & Pan, Rong, 2018. "A nonparametric Bayesian network approach to assessing system reliability at early design stages," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 57-66.
    3. Wenjie Lu & Jiazheng Li & Yifan Li & Aijun Sun & Jingyang Wang, 2020. "A CNN-LSTM-Based Model to Forecast Stock Prices," Complexity, Hindawi, vol. 2020, pages 1-10, November.
    4. Wang, Hongwei & Liu, Yaqi & Mu, Zongyi & Xiang, Jiawei & Li, Jian, 2023. "Real-time precision reliability prediction for the worm drive system supported by digital twins," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    5. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Haoyuan, Shen & Yizhong, Ma & Chenglong, Lin & Jian, Zhou & Lijun, Liu, 2023. "Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
    8. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    9. Tao, Tao & Zio, Enrico & Zhao, Wei, 2018. "A novel support vector regression method for online reliability prediction under multi-state varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 35-49.
    10. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    11. Yan, Shen & Shao, Haidong & Min, Zhishan & Peng, Jiangji & Cai, Baoping & Liu, Bin, 2023. "FGDAE: A new machinery anomaly detection method towards complex operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    12. Liang, Zhenglin & Li, Yan-Fu, 2023. "Holistic Resilience and Reliability Measures for Cellular Telecommunication Networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    13. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
    14. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C., 2022. "Bayesian framework for reliability prediction of subsea processing systems accounting for influencing factors uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    15. Huang, Cheng-Hao & Huang, Ding-Hsiang & Lin, Yi-Kuei, 2023. "Network reliability prediction for random capacitated-flow networks via an artificial neural network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    16. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
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