A Review on Rain Signal Attenuation Modeling, Analysis and Validation Techniques: Advances, Challenges and Future Direction

Abstract

Radio waves are attenuated by atmospheric phenomena such as snow, rain, dust, clouds, and ice, which absorb radio signals. Signal attenuation becomes more severe at extremely high frequencies, usually above 10 GHz. In typical equatorial and tropical locations, rain attenuation is more prevalent. Some established research works have attempted to provide state-of-the-art reviews on modeling and analysis of rain attenuation in the context of extremely high frequencies. However, the existing review works conducted over three decades (1990 to 2022), have not adequately provided comprehensive taxonomies for each method of rain attenuation modeling to expose the trends and possible future research directions. Also, taxonomies of the methods of model validation and regional developmental efforts on rain attenuation modeling have not been explicitly highlighted in the literature. To address these gaps, this paper conducted an extensive literature survey on rain attenuation modeling, methods of analyses, and model validation techniques, leveraging the ITU-R regional categorizations. Specifically, taxonomies in different rain attenuation modeling and analysis areas are extensively discussed. Key findings from the detailed survey have shown that many open research questions, challenges, and applications could open up new research frontiers, leading to novel findings in rain attenuation. Finally, this study is expected to be reference material for the design and analysis of rain attenuation.

Energy and Spectral Efficiencies of Cell-Free Millimeter-Wave Massive MIMO Systems Under Rain Attenuation Based on Ray Tracing Simulations

“…in which t, τ , Φ and Θ denote the absolute time, delay, angle of arrival (AoA) and angle of departure (AoD) domains, respectively. In turn, p r;m,k,n is the normalized power, calculated by means of (23), and τ m,k,n is the delay of the n-th received ray. In the angle domains, Θ m,k,n = [θ a;m,k,n , ϕ a;m,k,n ] T and Φ m,k,n = [θ d;m,k,n , ϕ d;m,k,n ] T are the AoA and AoD vectors, respectively, with (θ a;m,k,n , θ d;m,k,n ) expressing azimuth angles and (ϕ a;m,k,n , ϕ d;m,k,n ) expressing elevation angles.…”

“…Analyzing the expression in (23), it can be seen that the power of the received ray depends on the attenuation by rain ρ r in a given rainy regime with rate C r . Therefore, denoting the received rays powers as a function of the rainfall rate by p r;m,k,n (C r ), one can rewrite the value of β m,k as…”

“…[22]- [24]. Wave attenuation in a medium with a random distribution of raindrops depends on the characteristics of the transmitted signal, such as frequency and wave polarization, as well as on the properties of the rain, such as the drop size distribution (DSD) and the rain rate [23]. An analytical solution for rain attenuation can be calculated from Mie scattering theory, which applies the DSD to define the rain specific attenuation (dB/km) [25].…”

Seasonal Variation of Total Attenuation between Airborne Platform and Earth Station in South-West Region, Nigeria

“…Among the hydrometeors found at the atmosphere, rain is the major factor that causes fading in system of satellite communication [4][5][6]. Many researchers have estimated path loss on communication between satellite in space and ground stations using distinct models in different regions of the word in the past four decades [7][8][9]. However, path loss on earth-space or airborne-space link have not been given much attention.…”

Preliminary rain rate statistics with one‐minute integration time for radio propagation uses in Venezuela

“…Introduction: Although wireless communications systems operating at high frequencies (in general, above 10 GHz) offer high bandwidth, their performance is significantly affected by atmospheric phenomena such as rain, snow, ice, clouds, and fog [1], with rain causing severe fading of the signal; therefore, as the inaccuracy in the information about the precipitation rate at the location of interest decreases, the reliability of the values obtained from the rain attenuation prediction models will increase. In this regard, it is necessary to have preferably local measurements of the precipitation rate, specifically with an integration time of at least 1 min [2].…”