Statistical Learning for Service Quality Estimation in Broadband PLC AMI

Abstract

In this paper, we propose a method to estimate communication performance for the advanced metering infrastructure that employs the power line communication (PLC) technology. Using bit-per-symbol signals from the PLC network management system, we estimate a PLC model quality in terms of packet success rate based on statistical learning. We also verify the accuracy of the estimations by comparing them with measured communication test results at test sites. Finally, from the packet success rate estimate, the qualities of services, such as meter readings and time-of-use pricing data downloading under several metering protocol sequences, are investigated through a mathematical analysis, and numerical results are provided.

Keywords

• advanced metering infrastructure (AMI)
• network management system (NMS)
• power line communication (PLC)
• service quality analysis
• statistical learning