http://www.cnr.it/ontology/cnr/individuo/prodotto/ID236076
A novel method for the statistical prediction of rain attenuation in site diversity systems : theory and comparative testing against experimental data (Articolo in rivista)
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- A novel method for the statistical prediction of rain attenuation in site diversity systems : theory and comparative testing against experimental data (Articolo in rivista) (literal)
- Anno
- 1998-01-01T00:00:00+01:00 (literal)
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- A.V. Bosisio, C. Riva (literal)
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- Dipartimento di Elettronica e Informazione, Politecnico di Milano (literal)
- Titolo
- A novel method for the statistical prediction of rain attenuation in site diversity systems : theory and comparative testing against experimental data (literal)
- Abstract
- Site diversity is a promising technique to overcome severe attenuation on earth-space communication
links. This technique is based on the experimental evidence that intense rain cells are limited in extent,
so that the replacement of one station with two or more stations separated by a few kilometres may
substantially reduce the outage probability of the communication system.
In this paper the physical EXCELL rain cell model, developed at our laboratory for the prediction
of single-site rain attenuation statistics, has been extended to the case of diversity configuration systems.
For comparison purposes, two among the best-performing models found in the literature, proposed by
Hodge and Matricciani, have been considered. The values predicted by the three models have been
compared against data of 41 experiments with a maximum separation distance between ground stations
of 15 km, collected both from the ITU-R databank and from the open literature. This site separation
range is the most interesting one, because attenuations in diversity paths tend to be uncorrelated above
20-30 km and hence the benefit obtained by a diversity scheme becomes marginal. The measured and
predicted 'relative diversity gains' at various time percentages have been compared: the RMS values
of relative gain error show that EXCELL is the best-performing model. (literal)
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