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Recently, the need of deploying new wireless networks for smart gas meteringhas raised the problem of radio planning in the169 MHz band. Unluckily,software tools commonly adopted for radio planning in cellular communicationsystems cannot be employed to solve this problem because of the substantiallylower transmission frequencies characterizing this application. In thismanuscript a novel data-centric solution, based on the use of support vectormachine techniques for classification and regression, is proposed. Our methodrequires the availability of a limited set of received signal strengthmeasurements and the knowledge of a three-dimensional map of the propagationenvironment of interest, and generates both an estimate of the coverage areaand a prediction of the field strength within it. Numerical results referringto different Italian villages and cities evidence that our method is able toachieve good accuracy at the price of an acceptable computational cost and of alimited effort for the acquisition of measurements in the consideredenvironments.

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