On the Application of Support Vector Machines to the Prediction of Propagation Losses at 169 MHz for Smart Metering Applications

TL;DR

This paper presents a support vector machine-based approach for predicting radio propagation losses at 169 MHz to aid smart metering network planning, requiring minimal measurements and environmental data.

Contribution

It introduces a novel data-centric method using SVMs for propagation prediction at low frequencies, tailored for smart metering applications.

Findings

Achieves good accuracy in predicting coverage and field strength.

Requires limited measurement data and environmental mapping.

Offers computationally efficient predictions.

Abstract

Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the169 MHz band. Unluckily, software tools commonly adopted for radio planning in cellular communication systems cannot be employed to solve this problem because of the substantially lower transmission frequencies characterizing this application. In this manuscript a novel data-centric solution, based on the use of support vector machine techniques for classification and regression, is proposed. Our method requires the availability of a limited set of received signal strength measurements and the knowledge of a three-dimensional map of the propagation environment of interest, and generates both an estimate of the coverage area and a prediction of the field strength within it. Numerical results referring to different Italian villages and cities evidence that our method is able to achieve good accuracy at the price of an acceptable computational cost and of a limited effort for the acquisition of measurements in the considered environments.