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Centralized Rainfall Estimation Using Carrier to Noise of Satellite Communication Links
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg.ORCID iD: 0000-0003-2298-6774
2018 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 36, no 5, p. 1065-1073Article in journal (Refereed) Published
Abstract [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. © 1983-2012 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 36, no 5, p. 1065-1073
Keywords [en]
learning (artificial intelligence);neural nets;rain;satellite communication;centralized rainfall estimation;satellite communication links;real-time rainfall estimation;carrier-to-noise power ratio measurements;broadband satellite communication network;forward link;return link;Ka-band scenarios;signal processing;machine learning techniques;artificial neural network;rain events;signal attenuation;rainfall rate;Rain;Satellites;Estimation;Broadband communication;Satellite communication;Attenuation;Satellite communication;rainfall estimation;microwave propagation;Ka-band;broadband communication;artificial neural networks
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-258969DOI: 10.1109/JSAC.2018.2832798ISI: 000439994600010Scopus ID: 2-s2.0-85046429185OAI: oai:DiVA.org:kth-258969DiVA, id: diva2:1350563
Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-10-24Bibliographically approved

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  • apa
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