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.
Highlights
By directly predicting physical layer parameters computed within small time windows and evaluating link quality according to the mapping models between such parameters and packet reception ratio (PRR), the agility could be effectively improved without sacrificing the accuracy of prediction
This paper proposes a more effective link quality prediction method RNN-Link Quality Indicator (LQI), which adopts Recurrent Neural Network (RNN) to predict LQI counted within small time windows, and evaluates link quality according to the fitting model of LQI and PRR
Experimental results show that RNN-LQI is more accurate under different link qualities
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. By directly predicting physical layer parameters computed within small time windows and evaluating link quality according to the mapping models between such parameters and PRR, the agility could be effectively improved without sacrificing the accuracy of prediction. This paper proposes a more effective link quality prediction method RNN-LQI, which adopts Recurrent Neural Network (RNN) to predict LQI counted within small time windows, and evaluates link quality according to the fitting model of LQI and PRR. The advantages of this method are mainly two-fold: Firstly, it makes use of the short-term memory characteristics of RNN to accurately mine the inner relationship among LQI series. Conclusions are presented and suggestions are made for future works
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