In this manuscript a novel data-centric solution, based on the use of support vector machine techniques, is proposed to solve the problem of radio planning in the 169 MHz band. 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. Our numerical results evidence that our method is able to achieve a good accuracy at the price of an acceptable computational cost and of a limited effort for the acquisition of measurements.

On the use of support vector machines for the prediction of propagation losses in smart metering systems / Uccellari, Martino; Facchini, Francesca; Sola, Matteo; Sirignano, Emilio; Vitetta, Giorgio M.; Barbieri, Andrea; Tondelli, Simona. - (2016). ((Intervento presentato al convegno 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP 2016) tenutosi a Vietri sul Mare, Salerno, Italy nel September 13-16, 2016 [10.1109/MLSP.2016.7738887].

On the use of support vector machines for the prediction of propagation losses in smart metering systems

Uccellari, Martino;Sola, Matteo;Sirignano, Emilio;Vitetta, Giorgio M.;TONDELLI, SIMONA
2016

Abstract

In this manuscript a novel data-centric solution, based on the use of support vector machine techniques, is proposed to solve the problem of radio planning in the 169 MHz band. 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. Our numerical results evidence that our method is able to achieve a good accuracy at the price of an acceptable computational cost and of a limited effort for the acquisition of measurements.
2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP 2016)
Vietri sul Mare, Salerno, Italy
September 13-16, 2016
Uccellari, Martino; Facchini, Francesca; Sola, Matteo; Sirignano, Emilio; Vitetta, Giorgio M.; Barbieri, Andrea; Tondelli, Simona
On the use of support vector machines for the prediction of propagation losses in smart metering systems / Uccellari, Martino; Facchini, Francesca; Sola, Matteo; Sirignano, Emilio; Vitetta, Giorgio M.; Barbieri, Andrea; Tondelli, Simona. - (2016). ((Intervento presentato al convegno 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP 2016) tenutosi a Vietri sul Mare, Salerno, Italy nel September 13-16, 2016 [10.1109/MLSP.2016.7738887].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1118037
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