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. 2022 Feb 5;22(3):1212.
doi: 10.3390/s22031212.

Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links

Ming Xu  1 , Wei Liu  2 , Jinwei Xu  2 , Yu Xia  1 , Jing Mao  2 , Cheng Xu  1 , Shunren Hu  2 , Daqing Huang  1
Affiliations

Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links

Ming Xu et al. Sensors (Basel). .

Abstract

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the mapping models between such parameters and packet reception ratio (PRR). However, existing methods often ignore the temporal correlations of physical layer parameter series and rarely consider the influence of link fluctuations, which lead to more errors under moderate and sudden changed links with larger fluctuations. In view of these problems, this paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict the Link Quality Indicator (LQI) series, and then evaluates the link quality according to the fitting model of LQI and PRR. This method accurately mines the inner relationship among LQI series with the help of short-term memory characteristics of RNN and effectively deals with link fluctuations by taking advantage of the higher resolution of LQI in the transitional region. Compared with similar methods, RNN-LQI proves to be better under different link qualities. Especially under moderate and sudden changed links with larger fluctuations, the prediction error reduces at least by 14.51% and 13.37%, respectively. Therefore, the proposed method is more suitable for low power wireless links with more fluctuations.

Keywords: link quality indicator; link quality prediction; low power wireless links; recurrent neural network; temporal correlation; time series.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Theoretical Model of SNR and PRR.
Figure 2
Figure 2
Fitting Model of LQI and PRR.
Figure 3
Figure 3
Structure of the proposed RNN-LQI.
Figure 4
Figure 4
Structure of RNN.
Figure 5
Figure 5
Structure of the LQI Predictor based on RNN.
Figure 6
Figure 6
Predicted LQI vs. measured LQI. (a) good links, (b) moderate links, (c) bad links, (d) sudden changed links.
Figure 7
Figure 7
Performance comparison under good links. (a) RNN-LQI, (b) WNN-LQI, (c) RNN-SNR, (d) WNN-SNR.
Figure 8
Figure 8
Performance comparison under moderate links. (a) RNN-LQI, (b) WNN-LQI, (c) RNN-SNR, (d) WNN-SNR.
Figure 9
Figure 9
Performance comparison under bad links. (a) RNN-LQI, (b) WNN-LQI, (c) RNN-SNR, (d) WNN-SNR.
Figure 10
Figure 10
Performance comparison under sudden changed links. (a) RNN-LQI, (b) WNN-LQI, (c) RNN-SNR, (d) WNN-SNR.
Figure 11
Figure 11
CDF of the RMSE for PRR prediction under different link qualities. (a) good links, (b) moderate links, (c) bad links, (d) sudden changed links.
Figure 12
Figure 12
Convergence when training the model on desktop.
Figure 13
Figure 13
Execution overhead of the proposed method on TelosB.
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