Reference : Centralized Rainfall Estimation using Carrier-to-Noise of Satellite Communication Links
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
http://hdl.handle.net/10993/36671
Centralized Rainfall Estimation using Carrier-to-Noise of Satellite Communication Links
English
Gharanjik, Ahmad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Shankar, Bhavani mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Zimmer, Frank mailto []
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2017
IEEE Journal on Selected Areas In Communications
IEEE
Special Issue on Satcom
Yes (verified by ORBilu)
International
0733-8716
[en] Rainfall Estimation ; Satellite communication ; Neural Networks
[en] In this paper, we present a centralized method for real-time rainfall estimation using carrier-to-noise power ratio (C/N) measurements from broadband satellite communication networks. The C/N data of both forward link and return link are collected by the gateway station from the user terminals in the broadband satellite communication network and stored in a database. The C/N for such Ka-band scenarios is impaired mainly by the rainfall. Using signal processing and machine learning techniques, we develop an algorithm for real-time rainfall estimation. Extracting relevant features from C/N, we use artificial neural network in order to distinguish the rain events from dry events. We then determine the signal attenuation corresponding to the rain events and examine an empirical relationship between rainfall rate and signal attenuation. Experimental results are promising and prove the high potential of satellite communication links for real environment monitoring, particularly rainfall estimation.
Researchers ; Professionals
http://hdl.handle.net/10993/36671
10.1109/JSAC.2018.2832798
H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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