Dynamic Rain Attenuation Model for Millimeter Wave Network Analysis

Peric, Miroslav; Peric, Dragana; Todorović, Branislav M.; Popović, Miroslav

doi:10.1109/twc.2016.2624729

Cited by 19 publications

(10 citation statements)

References 22 publications

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“…As clarified in the last paragraph, rain characteristics in tropical regions may cause a real obstacle against the propagation of mm‐wave signals. Raindrops may interact with the propagated signal by imposing various physical effects such as scattering, reflection, absorption, depolarization, and rain temperature . Each raindrop will act as an obstacle facing the microwave signal, particularly, when raindrop size is comparable to the signal wavelength as depicted in Figure .…”

Section: Precipitation Impact On Mm‐wavementioning

confidence: 99%

Rain attenuation and worst month statistics verification and modeling for 5G radio link system at 26 GHz in Malaysia

Shayea

Nissirat

Nisirat

et al. 2019

Trans Emerging Tel Tech

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The explosive daily dependence on wireless communication services necessitates the research to establish ultrawideband communication systems with ultrahigh bit rate transmission capabilities. The advent of the fifth-generation (5G) microwave link transmitting at millimeter-wave (mm-wave) frequency band is a promising technology to accommodate the escalating demand for wireless services. In this frequency band, however, the behavior of the transmission channel and its climatic properties are a major concern. This is of particular importance in tropical regions where the climate is mainly rainy with large raindrop size and high rainfall rate that may interact destructively with the propagating signal and cause total attenuation for the signal. International Telecommunication Union (ITU) introduced a global rain attenuation model to characterize the effect of rain on the propagating signal at a wideband of frequencies. The validity of this model in tropical regions is still an open question for research. In this paper, real measurements are conducted at Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia, to investigate the impact of rain on the propagation of mm-waves at 26 GHz over the microwave 5G radio link system. Rainfall rate and rain attenuation data sets are collected for one year at one sample per min sampling rate. Both data sets are used to estimate signal propagation conditions in comparison to the ITU model prediction. From the presented results, it is found that at 0.01% percentage of time and rainfall rate of about 120 mm/hr, the propagated signal would experience 26.2 dB losses per kilometer traveled. In addition, there is a significant deviation between the empirical estimation of the worst month parameters and the ITU worst month parameter prediction. Similarly, rainfall rate and rain attenuation estimated through the ITU model imposes a large deviation as compared with the measurements. Furthermore, more accurate empirical worst month parameters are proposed that yielded more accurate estimation of the worst month rainfall and rain attenuation predictions in comparison to the ITU model predictions. Trans Emerging Tel Tech. 2019;30:e3697. wileyonlinelibrary.com/journal/ett

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Section: Precipitation Impact On Mm‐wavementioning

confidence: 99%

Rain attenuation and worst month statistics verification and modeling for 5G radio link system at 26 GHz in Malaysia

Shayea

Nissirat

Nisirat

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…This model is also referred to as a dynamic model [ 66 ]. It depends on the cumulative distribution function of the rain intensity of the area of interest, the number of rain events in which the rain intensity threshold is exceeded, the rain advection vector intensity, and the rain advection vector azimuth.…”

Section: Rain Attenuation Models: Terrestrial Linksmentioning

confidence: 99%

A Survey of Rain Attenuation Prediction Models for Terrestrial Links—Current Research Challenges and State-of-the-Art

Samad

Diba

Choi

2021

Sensors

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Millimeter-wave (30–300 GHz) frequency is a promising candidate for 5G and beyond wireless networks, but atmospheric elements limit radio links at this frequency band. Rainfall is the significant atmospheric element that causes attenuation in the propagated wave, which needs to estimate for the proper operation of fade mitigation technique (FMT). Many models have been proposed in the literature to estimate rain attenuation. Various models have a distinct set of input parameters along with separate estimation mechanisms. This survey has garnered multiple techniques that can generate input dataset for the rain attenuation models. This study extensively investigates the existing terrestrial rain attenuation models. There is no survey of terrestrial rain mitigation models to the best of our knowledge. In this article, the requirements of this survey are first discussed, with various dataset developing techniques. The terrestrial links models are classified, and subsequently, qualitative and quantitative analyses among these terrestrial rain attenuation models are tabulated. Also, a set of error performance evaluation techniques is introduced. Moreover, there is a discussion of open research problems and challenges, especially the exigency for developing a rain attenuation model for the short-ranged link in the E-band for 5G and beyond networks.

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“…As shown in (4), the total rain attenuation A is obtained by integration along the entire path length L , if rain intensity varies along the transceiver link denoted as R ( l )

A = \int_{0}^{L} k (f) \cdot R {(l)}^{a (f)} d l .

The rain intensity actually is not uniformly distributed. In order to describe the non‐uniformity of rain intensity, the rain cell model called EXCELL [41–43] as expressed in (5) is adopted in the study. In this model, rain intensity is modelled as a function of distance from the rain cell centre

R = R_{\max} \cdot exp false(- false(ρ / ρ_{0} false) false)^{2 / k_{0}},

where

R_{\max}

is the rain intensity in the cell centre (mm/h),

ρ

is the distance apart from its rain cell centre (km),

ρ_{0}

is the characteristic rain cell radius (km), and k 0 is the rain cell shape coefficient.…”

Section: Modelsmentioning

confidence: 99%

“…However, when using the time varying rain attenuation model, the time series for rainfall intensities are required to be measured. For the sake of simplicity without loss of generality, in this study, we use the EXCELL model [41–47] by introducing the speed of wind and rainfall intensity into the model. EXCELL model was tested in some literature, e.g.…”

Section: Introductionmentioning

confidence: 99%

Path loss modification and multi‐user capacity analysis by dynamic rain models for 5G radio communications in millimetre waves

et al. 2019

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Dynamic Rain Attenuation Model for Millimeter Wave Network Analysis

Cited by 19 publications

References 22 publications

Rain attenuation and worst month statistics verification and modeling for 5G radio link system at 26 GHz in Malaysia

Rain attenuation and worst month statistics verification and modeling for 5G radio link system at 26 GHz in Malaysia

A Survey of Rain Attenuation Prediction Models for Terrestrial Links—Current Research Challenges and State-of-the-Art

Path loss modification and multi‐user capacity analysis by dynamic rain models for 5G radio communications in millimetre waves

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