Experimental Performance Analysis of Hardware-Based Link Quality Estimation Modelling Applied to Smart Grid Communications

Abstract

The smart grid is the modern electricity grid, which significantly improves the efficiency, reliability, and sustainability of electricity transmission systems. The advanced metering infrastructure (AMI) system, which is the essential system in the smart grid, enables real-time data collection and data analysis obtained from smart meters (SMs) and other devices through last-mile communication networks. In this paper, the hardware-based link quality estimation (LQE) was modeled, namely an SNR-based model, a mapping model, and an RSSI- and PRR-based logistic regression model, and their performance was then evaluated by the root mean-squared error (RMSE) with the empirical data. The SNR-based and mapping models were formulated by the packet error probability, whereas the RSSI- and PRR-based logistic regression model was formulated by the empirical data fitting. The RSSI- and PRR-based logistic regression model outperformed the other two models, with an RMSE difference of 111–122%. These LQE models can be implemented on SMs or modems to monitor the reliability and efficiency of the AMI last-mile communication network.