Rain attenuation prediction model for terrestrial links incorporating wet antenna effects

Zhang, Xin; Zhao, Zhenwei; Wu, Zhensen; Lin, Leke; Lü, C.; Sun, Mingchen; Zhu, Qinglin

doi:10.1049/mia2.12384

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…In addition to including an account for attenuation due to antenna wetness, the paper also reported the important conclusion that path reduction factor prescribed by ITU-R P.530-17 gives inaccurate outcomes when utilized for short-range links. This same conclusion featured in [24], in which the rain attenuation proposed for terrestrial line-of-sight links also includes an account for wet antenna effects and moderates inaccuracies due to the use of the conventional path adjustment factor with a rainfall adjustment factor.…”

Section: A Literature Reviewmentioning

confidence: 52%

On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm

Adekola,

Ayorinde,

Muhammed

et al. 2023

JCIS

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A computationally inexpensive, analytically simple, and remarkably efficient rain attenuation prediction algorithm is presented in this paper. The algorithm, here referred to as the Quasi Moment-Method (QMM), has only two main requirements for its implementation. First, rain attenuation measurement data (terrestrial or slant path) for the site of interest must be available; and second, a model, known to have predicted attenuation for any site to a reasonable level of accuracy (base model), and whose analytical format can be expressed as a linear combination of its parameters, is also required. An important novelty introduced by the QMM algorithm is a normalization scheme, through which a modelling difficulty concerning exceedance probabilities outside a 0.01 to 1.00 range, is eliminated. Model validation and performance evaluation using a comprehensive set of data available from the literature clearly demonstrated that the QMM models consistently improved base model performance by more than 90%; and outperformed all published ‘best fit’ models with which they were compared.

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Section: A Literature Reviewmentioning

confidence: 52%

On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm

Adekola,

Ayorinde,

Muhammed

et al. 2023

JCIS

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“…A similar modeling approach is proposed in [38], which considers the effect of additional contributions to the path attenuation on measurements, where AADD is ascribed only to the wet antenna effect. Indeed, also the work in [38] proposes a rain attenuation statistics model that includes a path reduction factor limited to 1. In this work, 𝐴 𝐴𝐷𝐷 is defined as the combination of a fixed term, AF, and of a time variant one, AWA(t).…”

Section: G Path Reduction Factor Calculation: Statistical Approachmentioning

confidence: 99%

“…Though equation ( 17) offers quite a simplified approach to model additional attenuations contributions due to systeminduced effects, it appears to provide a possible explanation as to why PRFITU is characterized by such a steep increase as L reduces: though this remains an open question, the attenuation measurements included in the DBSG3 database and used to derive PRFITU might not actually be representative only of the effects induced by precipitation [38]. Moreover, in some cases, additional factors might further contribute to an increased value of PRF as derived from measurements.…”

Section: G Path Reduction Factor Calculation: Statistical Approachmentioning

confidence: 99%

The Path Reduction Factor for the Prediction of Rain Attenuation Affecting Short EHF Terrestrial Links

Capelletti,

Riva,

Roveda

et al. 2024

IEEE Open J. Antennas Propag.

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The path reduction factor (PRF), a key element of semi-empirical rain attenuation statistics prediction models, is investigated to shed some light on its value for links shorter than 1 km. PRF is here calculated from simulations underpinned by the use of the Enhanced Synthetic Storm Technique (E-SST) to take into account the rain rate spatial distribution along the path. This novel approach, in contrast with the more customary one of inferring a PRF model from measurements, offers the advantage of avoiding considering any unwanted additional attenuation not due to precipitation, but typically linked to systeminduced effects. Results indicate that, as expected, PRF reduces with the increase in the rain rate R and in the path length L, and they also reveal quite a marginal dependence on the operational frequency. Most importantly, the outcomes highlight that the maximum values of PRF only slightly exceeds 1 and, in addition, they provide a possible explanation as to why, on the contrary, the path reduction factor defined in the Recommendation ITU-R P.530-18 is characterized by a steep increase as L reduces.

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Rain attenuation prediction model for terrestrial links incorporating wet antenna effects

Cited by 2 publications

References 29 publications

On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm

On the Quasi-Moment-Method as a Rain Attenuation Prediction Modeling Algorithm

The Path Reduction Factor for the Prediction of Rain Attenuation Affecting Short EHF Terrestrial Links

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