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LSTM based link quality confidence interval boundary prediction for wireless communication in smart grid

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Abstract

The smart grid will play an important role in the future city to support the diversified energy supply. Wireless communication, the most cost-effective alternative to the traditional wire-lines, promises to provide ubiquitous bi-direction information channel for smart grid devices. However, due to the complex environment that smart grid devices located in, the wireless link is easily been interfered with and therefore appears strong stochastic features. Considering different smart grid application traffics have different and strict reliability requirements, the confidence interval lower boundary is more suitable to represent the worst-case reliability of the stochastic wireless link quality and trustworthy for judging whether the link quality is qualified for the next transmission. In this paper, we propose a Long-Short-Term-Memory (LSTM) based link quality confidence interval lower boundary prediction for the smart grid. According to the analysis of the characteristics of the wireless link, we employ the wavelet denoising algorithm to decompose the signal-to-noise ratio time series into the deterministic part and the stochastic part for training two LSTM neural networks. Then, the deterministic part and the variance of the stochastic part are predicted respectively. Lastly the confidence interval boundary is calculated. To verify the performance of the proposed LQP method, real-world experiments are carried out and the results show that our method is more accurate and trustworthy in comparison with other link quality prediction methods.

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Authors and Affiliations

  1. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China

    Wei Sun, Pengyu Li, Xue Xue, Qiyue Li & Haiyan Zhang

  2. Department of Mathematical and Systems Engineering, Shizuoka University, Shizuoka, Japan

    Zhi Liu

  3. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China

    Junbo Wang

Authors
  1. Wei Sun
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  2. Pengyu Li
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  3. Zhi Liu
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  4. Xue Xue
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  5. Qiyue Li
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  6. Haiyan Zhang
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  7. Junbo Wang
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Correspondence to Qiyue Li.

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This work was supported by National Natural Science Foundation of China (51877060) and the Fundamental Research Funds for the Central Universities of China (PA2019GDQT0006 and JZ2018HGTB0253).

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Sun, W., Li, P., Liu, Z. et al. LSTM based link quality confidence interval boundary prediction for wireless communication in smart grid. Computing 103, 251–269 (2021). https://doi.org/10.1007/s00607-020-00816-7

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  • DOI: https://doi.org/10.1007/s00607-020-00816-7

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