Rainfall estimation based on the impact of rain on electromagnetic waves is a novel methodology that has had notable advancements during the last few years. Many studies conducted on this topic in the past considered only the electromagnetic waves with frequencies greater than 10 GHz since the rainfall impact on the electromagnetic wave attenuation is reduced at lower frequencies. Over the last few years, some authors have demonstrated that there can be a non-negligible attenuation even on the signals received on a global system for mobile communications (GSM) mobile terminal in presence of rain. In this paper, we propose a new classification method based on a probabilistic neural network to obtain an accurate classification between four rainfall intensities (no rain, weak rain, moderate rain and heavy rain). The innovative rainfall classification method is based on three RSL (received signal level) local features of the 4G/LTE: the instantaneous RSL, the average RSL value and its variance calculated by using a sliding window. The proposed method exhibits good performance, obtaining an overall correct classification rate of 96.7%. Almost all papers on this topic present in the literature focus on electromagnetic waves with frequencies greater than 10 GHz, in which the rain impact is more relevant, according to the rain attenuation model. However, only the 4G/LTE signal has such widespread geographic coverage, so the proposed classification method can provide noticeable improvements in the creation of rainfall maps with higher spatial resolution.

Rainfall Estimation Based on the Intensity of the Received Signal in a LTE/4G Mobile Terminal by using a Probabilistic Neural Network

Beritelli, F.;Capizzi, G.;Sciuto, G. Lo;Napoli, C.;Scaglione, F.
2018-01-01

Abstract

Rainfall estimation based on the impact of rain on electromagnetic waves is a novel methodology that has had notable advancements during the last few years. Many studies conducted on this topic in the past considered only the electromagnetic waves with frequencies greater than 10 GHz since the rainfall impact on the electromagnetic wave attenuation is reduced at lower frequencies. Over the last few years, some authors have demonstrated that there can be a non-negligible attenuation even on the signals received on a global system for mobile communications (GSM) mobile terminal in presence of rain. In this paper, we propose a new classification method based on a probabilistic neural network to obtain an accurate classification between four rainfall intensities (no rain, weak rain, moderate rain and heavy rain). The innovative rainfall classification method is based on three RSL (received signal level) local features of the 4G/LTE: the instantaneous RSL, the average RSL value and its variance calculated by using a sliding window. The proposed method exhibits good performance, obtaining an overall correct classification rate of 96.7%. Almost all papers on this topic present in the literature focus on electromagnetic waves with frequencies greater than 10 GHz, in which the rain impact is more relevant, according to the rain attenuation model. However, only the 4G/LTE signal has such widespread geographic coverage, so the proposed classification method can provide noticeable improvements in the creation of rainfall maps with higher spatial resolution.
2018
Attenuation; Databases; Estimation; Feature extraction techniques; LTE; Probabilistic neural network; Radio signal attenuation; Rain; Rainfall estimation; Spaceborne radar; Computer Science (all); Materials Science (all); Engineering (all)
File in questo prodotto:
File Dimensione Formato  
08365880.pdf

accesso aperto

Tipologia: Versione Editoriale (PDF)
Dimensione 5.48 MB
Formato Adobe PDF
5.48 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/329594
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 52
  • ???jsp.display-item.citation.isi??? 32
social impact