Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the 169 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 characterising this application. In this study, a novel data-centric solution, based on the use of support vector machine techniques for classification and regression, is illustrated. The proposed 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. Various numerical results show that the proposed 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.

On the application of support vector machines to the prediction of propagation losses at 169 MHz for smart metering applications / Martino Uccellari, Author(s):; Facchini, Francesca; Sola, Matteo; Sirignano, Emilio; Vitetta, Giorgio M.; Barbieri, Andrea; Tondelli, Stefano. - In: IET MICROWAVES, ANTENNAS & PROPAGATION. - ISSN 1751-8725. - 12:3(2018), pp. 302-312. [10.1049/iet-map.2017.0364]

On the application of support vector machines to the prediction of propagation losses at 169 MHz for smart metering applications

Francesca Facchini;Matteo Sola;Emilio Sirignano;Giorgio M. Vitetta;
2018

Abstract

Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the 169 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 characterising this application. In this study, a novel data-centric solution, based on the use of support vector machine techniques for classification and regression, is illustrated. The proposed 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. Various numerical results show that the proposed 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.
2018
12
3
302
312
On the application of support vector machines to the prediction of propagation losses at 169 MHz for smart metering applications / Martino Uccellari, Author(s):; Facchini, Francesca; Sola, Matteo; Sirignano, Emilio; Vitetta, Giorgio M.; Barbieri, Andrea; Tondelli, Stefano. - In: IET MICROWAVES, ANTENNAS & PROPAGATION. - ISSN 1751-8725. - 12:3(2018), pp. 302-312. [10.1049/iet-map.2017.0364]
Martino Uccellari, Author(s):; Facchini, Francesca; Sola, Matteo; Sirignano, Emilio; Vitetta, Giorgio M.; Barbieri, Andrea; Tondelli, Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1155117
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