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

Sensors

卷:22

Yu Xia¹  Daqing Huang¹  Ming Xu¹  Cheng Xu¹  Jinwei Xu²  Shunren Hu²  Jing Mao²  Wei Liu² 

[1] College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

[2] School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China;

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

DOI  :  10.3390/s22031212

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown