sustainability
Review
A Review on Rain Signal Attenuation Modeling, Analysis and
Validation Techniques: Advances, Challenges and
Future Direction
Emmanuel Alozie 1 , Abubakar Abdulkarim 2,3 , Ibrahim Abdullahi 4 , Aliyu D. Usman 5 , Nasir Faruk 6,7 ,
Imam-Fulani Yusuf Olayinka 1 , Kayode S. Adewole 8 , Abdulkarim A. Oloyede 1 , Haruna Chiroma 9 ,
Olugbenga A. Sowande 1 , Lukman A. Olawoyin 1 , Salisu Garba 10 , Agbotiname Lucky Imoize 11,12, * ,
Abdulwaheed Musa 13,14 , Yinusa A. Adediran 15 and Lawan S. Taura 6
1
2
3
4
5
6
7
8
Citation: Alozie, E.; Abdulkarim, A.;
Abdullahi, I.; Usman, A.D.; Faruk, N.;
9
10
11
Olayinka, I.-F.Y.; Adewole, K.S.;
Oloyede, A.A.; Chiroma, H.;
12
Sowande, O.A.; et al. A Review on
Rain Signal Attenuation Modeling,
13
Analysis and Validation Techniques:
14
Advances, Challenges and Future
15
Direction. Sustainability 2022, 14,
*
Department of Telecommunication Science, University of Ilorin, Ilorin 240003, Nigeria
Department of Electrical Engineering, Ahmadu Bello University, Zaria 810107, Nigeria
Department of Electrical and Telecommunications Engineering, Kampala International University, Kansanga,
Kampala P.O. Box 20000, Uganda
Department of Electrical and Electronics Engineering Technology, Nuhu Bamalli Polytechnic,
Zaria PMB 1061, Nigeria
Department of Electronics and Telecommunication Engineering, Ahmadu Bello University,
Zaria 810107, Nigeria
Department of Physics, Sule Lamido University, Kafin Hausa PMB 048, Nigeria
Directorate of Information and Communication Technology, Sule Lamido University,
Kafin Hausa PMB 048, Nigeria
Department of Computer Science, University of Ilorin, Ilorin 240003, Nigeria
College of Computer Science and Engineering, University of Hafr Al Batin, Hafr Al Batin 39524, Saudi Arabia
Department of Computer Science, Sule Lamido University, Kafin Hausa PMB 048, Nigeria
Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka,
Lagos 100213, Nigeria
Department of Electrical Engineering and Information Technology, Institute of Digital Communication,
Ruhr University, 44801 Bochum, Germany
Department of Electrical and Computer Engineering, Kwara State University, Malete 241103, Nigeria
Institute for Intelligent Systems, University of Johannesburg, Johannesburg P.O. Box 524, South Africa
Department of Electrical and Electronics Engineering, University of Ilorin, Ilorin 240003, Nigeria
Correspondence: aimoize@unilag.edu.ng
11744. https://doi.org/10.3390/
su141811744
Academic Editor: Manuel
Fernandez-Veiga
Received: 15 August 2022
Accepted: 13 September 2022
Published: 19 September 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affiliations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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.
distributed under the terms and
conditions of the Creative Commons
Keywords: attenuation; rain; frequency; communication; millimeter wave; microwave; ITU-R
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Sustainability 2022, 14, 11744. https://doi.org/10.3390/su141811744
https://www.mdpi.com/journal/sustainability
Sustainability 2022, 14, 11744
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1. Introduction
The increasing demand for high data rate and capacity, and the inability of previous
generations to meet these demands, have motivated the research and development of the
5G communication network [1,2]. The 5G wireless network has promised to provide a
multi-Gigabit-per-second (Gbps) data rate with extremely low latency and better quality
of service (QoS) that would support critical services. These services comprise, but are not
limited to, the provision of e-health, especially in rural areas [3–5], equitable and inclusive
education [6–8], smart farming [9,10], and bridging of the digital divide [11–13].
Millimeter wave (mmWave) communication within the frequency range 30–300 GHz
has been proven to be the candidate band for 5G communication networks and beyond
due to the scarcity of spectrum frequency below 5 GHz (sub-6 GHz) [14–18]. The mmWave
band offers the security of communication transmissions and supports the large bandwidth
required to provide higher data rates for fronthaul, backhaul and short building-to-building
links [2,19–22]. However, the major drawback of the millimeter wave signal is its inability
to travel over a long distance due to its susceptibility to attenuation by various atmospheric
phenomena such as rain, foliage, and other atmospheric absorption [23–30].
Rain, among all other atmospheric phenomena, is the major source of microwave and
millimeter wave signal attenuation through absorption and scattering in terrestrial and
satellite communication links. When the operating frequency exceeds 10 GHz, the attenuation effect becomes very severe, particularly in the tropics, with tendencies of heavy
and thunderous rain droplets and depths [31–34]. Rainfall is a complex meteorological
phenomenon due to its inhomogeneous behavior in terms of duration, frequency of occurrence, and location. The inhomogeneous nature makes it highly unpredictable and
challenging when estimating its effect on link design. However, if the rainfall rate variation
throughout the entire signal path is known, the rain attenuation can be estimated from the
integration of the specific attenuation and path length [35,36]. Conventionally, rain rates
are measured using rain gauges, disdrometers, and weather radars. The data obtained from
such measuring instruments are usually used to develop models that help in predicting
and, in some cases, mitigating the effect of rain attenuation on the links.
Several researchers over the years have developed novel models and methods, in some
instances modifying existing ones for estimating rain rates across various frequencies and
climatic locations [37–43]; notably, the ITU-R has also developed a couple of models [44,45].
It is worthy to note that some of the climatic variables such as wind speed, rainfall intensity,
frequency, polarization, path length, temperature, humidity, etc., have impending effects
on rain attenuation. These thus expose the limit of validity of most of the aforementioned
models as these models attempt to evaluate the relationship between rain rate and path
length to obtain rain attenuation [46].
This paper aims to conduct a systematic review of rain attenuation models. The different prediction and mitigation models are broached, including the taxonomies in different
areas of rain attenuation modeling and analysis, noting research gaps and recommending
further directions of research. The noteworthy contributions of this review paper are
outlined as follows:
An extensive, systematic review of rain attenuation models for the past 30 years
(1990–2022) is provided.
A panoramic view of rain attenuation models and an exhaustive review of studies
that have utilized these models is presented, including a taxonomy that followed the
work of [47].
A comprehensive analysis of the total and specific attenuation based on various
atmospheric conditions and other impairments, including the radome, is discussed.
An exhaustive review of rain attenuation prediction using machine learning models
is presented, including a proposed taxonomy of these models.
An in-depth analysis and review of fade mitigation techniques is presented.
Critical open research issues and future research directions are identified for rain
attenuation and elaborated.
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The structure of the paper is as follows: The “Review of Previous Rain Attenuation
Models” Section 2 reviews prior review works on rain attenuation models. The “Background on Rain Attenuation” Section 3 summarizes the theory of rain attenuation, rain attenuation factors, and the various ways to obtain rain rate data, as well as the survey of rain
attenuation across regions. The “Rain Attenuation Models” Section 4 discusses and reviews
the various existing rain attenuation models. Furthermore, signal attenuation due to various atmospheric impairments and total propagation attenuation is examined in the “Total
Attenuation” Section 5. Similarly, the specific attenuation scenarios are presented in the
“Specific Attenuation” Section 6. The “Review of Different Methods of Model Validation”
Section 7 summarizes the various model validation techniques, including the properties
of the existing rain attenuation models. The various machine learning-based models are
presented and reviewed in the “Machine Learning-Based Rain Attenuation Prediction
Models” Section 8. The various fade mitigation techniques, including the weaknesses of
the ITU-R model for short distances, are discussed and reviewed in the “Fade Mitigation
Techniques For 5G” Section 9. The “Further Research Direction” Section 10 provides a clear
path for further research in rain attenuation. Finally, a concise conclusion is drawn in the
“Conclusion” Section 11.
2. Review of Previous Rain Attenuation Models
This section presents a systematic review of previous works on rain attenuation,
covering the last three decades, 1990 to 2022, with a binocular focus on review articles
only. The academic databases employed for this review include IEEE Explore, MDPI,
ACM Digital Library, Springer, Science Direct, and Google Scholar. These databases
are comprised of reliable and good quality peer-reviewed publications such as review
articles, research articles, and conference papers. The search term “rain attenuation review”
was used to query these databases to obtain relevant literature review articles published
between 1990 and 2022, from which 16,668 review publications were obtained across all
the selected databases, all written in the English language which had been chosen as one
of the inclusion criteria. To avoid duplicates, papers found on a generic database such as
Google Scholar were traced back to their respective publishing journal and counted under
that journal, rather than being counted under Google Scholar. Figure 1 shows the article
selection process used to screen the pool of articles to further reduce the search results.
Table 1 summarizes the number of articles obtained from the different databases consulted,
including the percentage in descending order in terms of relevance to the subject of interest.
Table 2 presents an overview of previous rain attenuation model reviews which includes
their objectives and findings. Table 3 provides a summary of the comparison between the
current survey and the existing ones.
Figure 1. Articles Selection Process.
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Table 1. Search Databases and Number of Articles.
S/N
Article Sources
URL
No. of Articles
Percentage (%)
1
Google Scholar
https://scholar.google.com/
6
37.50
2
IEEE Explore
https://ieeexplore.ieee.org/
5
31.25
3
MDPI
https://www.mdpi.com/
4
25.00
4
Springer
https://www.springer.com/gp
1
6.25
5
ACM Digital Library
https://dl.acm.org/
0
0.00
Total =
16
100
Table 2. Summary of Previous Rain Attenuation Review Publications.
Ref.
Title of Publication
Objectives
Findings
Year
[48]
Development of
rain-attenuation and rain-rate
maps for satellite system
design in the Ku and Ka bands
in Colombia
To present the review of some of the
most important rain-rate and
rain-attenuation
measurement campaigns.
The average deviation between
models and measurements is
approximately 30%, and no model is
suitable for all climate zones of
the world.
2004
[49]
Review of Rain Attenuation
Studies in Tropical and
Equatorial Regions in Brazil
To provide a review of rain
attenuation research works based on
measurements performed in tropical
and equatorial regions of Brazil.
Based on the reviews, it was found
that the available models are only
suitable for temperate climates and
not suitable for tropical and
equatorial climates.
2005
Effect of Rain on
Millimeter-Wave
Propagation—A Review
To review the impact of rain on
millimeter wave propagation.
The study briefly reviewed the Mie
theory, various drop size distributions
based on the point rain rate,
cross-polarization, statistical models,
rain attenuation models,
and frequency and path length scaling
for rain attenuation statistics.
2007
[51]
Variability of millimeter wave
rain attenuation and rain rate
prediction: A survey
To review the literature on rain
attenuation and rain rate prediction
methods proposed by researchers
around the globe to evaluate the
performance under varying
meteorological and topographical
conditions with a focus on the reports
made in the Indian subcontinent.
Among the contending models,
Garcia-Lopez’s model is suitable for
predicting rain attenuation in the
northern region of India due to its
simplicity and less complexity.
2007
[52]
Analysis and parameterization
of methodologies for the
conversion of rain-rate
cumulative distributions from
various integration times to
one minute
Review the main models used for
converting rain statistics from various
integration times to one minute.
Only conversion models with a
maximum of two parameters are
suitable for worldwide application, of
which the Lavergnat-Gole model is
recommended as the best for any
integration times and climate regions.
2009
[53]
A Review on Rain Attenuation
of Radio Waves
To understand rain attenuation
occurrences, how they can be
measured, and review all
measurement methods
developed so far.
Rain attenuation is mostly calculated
using empirical formulations relating
the rain rate with specific attenuation.
This method is significant only when
the frequency exceeds 5–10 GHz,
and also, rain drop-based modeling is
most accurate in terms of exactness.
2012
[54]
Review of Rain Attenuation
Studies in
Tropical and Equatorial
Regions in Malaysia:
An Overview
To review all previous research work
related to rain-induced attenuation for
microwave propagation in Malaysia’s
tropical climate.
Rain rate value and the regression
factor for the raindrop size
distribution vary in Malaysia based on
the region for measuring the
rain attenuation.
2013
[50]
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Table 2. Cont.
Ref.
Title of Publication
Objectives
Findings
Year
[55]
Precipitation and other
propagation impairments
effects microwave
and millimeter wave bands:
a mini survey
To review and discuss rain attenuation
models developed worldwide using
various measurement campaigns for
microwave and millimeter
wave frequencies.
The ITU-R model, when compared to
other prediction models, either underor over-estimates, especially for
tropical region measurement sites.
2019
[32]
Atmospheric Impairments and
Mitigation Techniques for
High-Frequency Earth-Space
Communication System in
Heavy Rain Region:
A Brief Review
To briefly review previous works on
the atmospheric effects, particularly
rain and clouds, on high-frequency
satellite communication.
The study presented research works to
distinguish scintillation from rain
attenuation. Then discussed and
reviewed different rain attenuation
models and their characteristics in
heavy rain regions. Also presented
were cloud and water vapor
attenuation models and then
discussed the different propagation
impairment mitigation techniques.
2019
[56]
Earth-to-Earth Microwave
Rain Attenuation
Measurements: A Survey on
the Recent Literature
Research challenges and future trends
are to conduct a systematic review of
rainfall measurement using
earth-to-earth microwave signal
attenuation from backhaul cellular
microwave links and experimental setup.
Microwave path attenuation is a
promising and reliable method for
estimating the rain rate. Also, factors
such as the wet antenna effects and
jitters caused by wind on antennas
may lead to significant errors too.
2020
[57]
Experimental Studies of
Slant-Path Rain Attenuation
Over Tropical and Equatorial
Regions: A Brief Review
Review and summarize the
performance of various rain
attenuation models validated against
satellite signal measurement in
tropical and equatorial regions.
Among the 33 models reviewed, none
was suitable for all locations and
percentage-exceedance levels. Still,
the ITU-R and DAH models are
suitable for low rain rates compared to
other models.
2021
[58]
An overview of rain
attenuation research in
Bangladesh
To review rain attenuation research
works, global research trends,
and research scope in Bangladesh.
Rain attenuation models that can be
used for tropical and subtropical
regions cannot be directly used over
Bangladesh without appropriate
testing and verification.
2021
[59]
Scaling of Rain Attenuation
Models: A Survey
To develop a rain attenuation scaling
technique taxonomy and review
research work according to the
taxonomy and perform a comparative
study on these techniques.
The study reviewed more than 17 rain
attenuation scaling models. SAM
model can estimate the spatial
distribution when the rain rate and
radio link are distributed uniformly.
However, a more sophisticated spatial
rainfall distribution is required.
[47]
A Survey of Rain Attenuation
Prediction Models for
Terrestrial Links—Current
Research Challenges and
State-of-the-Art
To conduct a comprehensive review of
the different rain attenuation
prediction models for terrestrial links
This study reviewed 18 rain
attenuation models based on the
survey. It found that no rain
prediction model can solely satisfy all
the geographic locations and climatic
variations over time.
[60]
A Survey of Rain Fade Models
for Earth–Space
Telecommunication
Links—Taxonomy, Methods,
and Comparative Study
To review different slant path rain
attenuation prediction models based
on different aspects such as rain
regions, rain structure, rainfall rate,
elevation angle.
This study reviewed more than 23 rain
attenuation models for satellite links,
and it found that models work well
for locations where they are
developed and might not function
well for other locations.
[61]
Rain Attenuation Prediction
Models in Microwave and
Millimeter Bands for Satellite
Communication System:
A Review
To review the rain rate integration
time, rain height, and rain attenuation
models for microwave and millimeter
bands satellite systems.
The study reviewed three classes of
rain rate integration time conversion
methods and then reviewed six rain
attenuation prediction models for
satellite-to-earth communication.
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Ref.
Empirical
Models
Statistical
Models
OptimizationBased Models
Physical
Models
Fade Slope
Models
Mitigation
Models
MachineLearning
Models
Table 3. Summary of Comparison between the Current and Existing Surveys.
[48]
✓
✓
×
✓
×
×
×
[49]
✓
✓
[50]
✓
✓
[51]
✓
✓
[52]
✓
✓
[53]
×
✓
[54]
✓
✓
[55]
✓
✓
[32]
×
✓
[56]
×
✓
[57]
✓
✓
[58]
×
×
[59]
×
[47]
✓
[60]
✓
[61]
✓
✓
Current Survey
✓
✓
×
×
×
×
×
×
×
×
✓
×
✓
×
×
✓
×
×
×
×
×
×
×
×
×
×
×
×
✓
×
×
×
×
✓
×
×
×
×
×
×
×
×
×
×
×
×
×
×
✓
✓
✓
✓
✓
×
✓
✓
×
✓
×
✓
✓
✓
✓
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
✓
×
×
×
×
✓
✓
From Table 3, it can be seen that most review works focused more on empirical and
statistical models without considering mitigation models as well as machine learningbased models for rain attenuation. Also, most of the review work that included the rain
attenuation prediction models did not consider the rain fade mitigation techniques and
vice versa. From the overall systematic review, it can be seen that there is very little review
work done on rain attenuation that shows the trend of work done in the research area and
proffers further direction. Hence, this paper aims to extensively review and analyze the
different existing prediction and mitigation models for rain attenuation, including machine
learning-based models, as well as provide further directions to bridge these existing gaps
3. Background on Rain Attenuation
This section provides a preliminary discussion on rain attenuation, which includes
the theory behind rain attenuation, rain attenuation factors, rain data gathering methods,
and spatial interpolation methods for estimating rain rate.
3.1. Theory of Rain Attenuation
In general, electromagnetic waves transport photons, which carry energy E = hν
where h is Planck’s constant and ν is the frequency of the emitted electromagnetic wave.
Absorption and dispersion occur as the wave travels through matter. Absorption is the
capacity of an atom and molecule to retain the energy conveyed by the photons. In the
case of dispersion, the retained energy of the photons is re-emitted out, taking various
paths with varying concentrations. The spatially dispersed waves are responsible for
𝐸=
𝜈
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scattering [56]. These activities caused electromagnetic signals to be attenuated by rain.
In this regard, the energy of the molecules𝐸can
expressed
= be
𝐸 (∧)
+ 𝐸 (𝑣)as+Equation
𝐸 (𝑗) + 𝐸(1):
𝐸
E M = EE (∧) + Ev (v) + ER (𝐸j) (∧)
+ ET
(1)
𝐸 (𝑣)
where E M is the energy
𝐸 (𝑗) of the molecule, EE (∧) is the electron energy of the molecule, Ev (v)
is the vibrational energy of the atom𝐸around the equilibrium position of the molecule, ER ( j)
is the rotational energy corresponding to the rotation of the molecule about its symmetry
axis, and ET is the translational motion energy of the molecule. The difference in energy
between a molecule’s initial and excited states is said to be equal to the absorbed energy of
the photon when the molecule changes its quantum level. Figure 2 illustrates that raindrops
can cause electromagnetic signals to be absorbed, scattered, diffracted, and depolarized.
Figure 2. Impact of Rain on Electromagnetic Wave Propagation [62].
As the frequency increases, the wavelength becomes smaller. When the wavelength
of the rain is a few mm less than the frequency, the attenuation increases. The Average
Raindrop Size (ARS) has a diameter of 1.67 mm while 10–100 GHz signals have wavelengths of 30–3 mm. A raindrop has an average diameter of 0.1–5 mm. In the case of
Rayleigh scattering by raindrops, known as the scattering function as given in Equation (2),
the droplet size is substantially less than the wavelength, which is satisfied for frequencies
up to 3 GHz. The function specifically applies to the raindrop scattering properties and is
affected by the radius of the raindrop, the shape of the raindrop, the complex permittivity,
and the frequency of the transmitted signal.
f =
ξ d − 1 π2 3
× 2 D
ξd + 2
λ
(2)
where ξ d is the complex permittivity of the droplet, D is the raindrop size, and λ is the
wavelength. Mie’s approximation for the scattering function is given as Equation (3):
jλ3
f = 3 2
π D
"
∞
∑ (2n + 1)( Mc )
n =1
#∗
(3)
where j is the imaginary unit and Mc = xn + yn is the Mie’s coefficients which are constituted of Bessel functions of order n. The vertical and horizontal polarization for the specific
rain attenuation can be expressed as Equation (4):
γh,v = 8.686·103 ·
2π
·lm
k
Z
f h,v ( D )· N ( D, Rr )dD
(4)
𝑛
Sustainability 2022, 14, 11744
𝑘
𝛾
,
= 8.686 ∙ 10 ∙
𝐷
2𝜋
∙ 𝑙𝑚
𝑘
𝑓 , (𝐷) ∙ 𝑁(𝐷, 𝑅 )𝑑𝐷
𝑁(𝐷, 𝑅)
8 of 65
.
⁄ N
where k is the propagation constant,
D is𝑅the
raindrop
( D, R) is the raindrop
)=
𝑁(𝐷,
8000 ∙ 𝑒size,. ∙and
size distribution which can be calculated using Equation (5) as:
𝑅
0.21
N ( D, Rr ) = 8000·e−4.1· D/Rr
(5)
where Rr denotes the rain rate in mm/h.
3.2. Rain Attenuation Factors
3.2.1. Path Length
𝐿
𝐴
The(𝛾path length is critical in determining Rain Attenuation A which can be estimated
by multiplying the effective propagation path length (L p ) and specific rain attenuation
(γ) as shown in Equation (6) [2].𝐿 Figure 3 depicts a conventional experimental setup to
measure rain attenuation, which consists of transmitter and receiver stations separated by
𝐴 = 𝛾𝐿
a distance or path length L p
A = γL p
(6)
Figure 3. A Conventional Experimental Setup to Estimate Rain Attenuation [56].
Specific attenuation is calculated using a 1-min cumulative distribution rain rate
expressed in decibels per kilometer (dB/km). The path length, typically determined at
one end, can be estimated by multiplying the path adjustment factor by the real distance.
However, it can be determined from Equation (6) with respect to the point rain rate [62].
According to [47], many models compute the effective propagation path length by employing a compensatory factor known as the path reduction factor or path adjustment factor.
3.2.2. Frequency and Polarization
The specific attenuation, γ (dB/km), expressed in terms of the rain rate, frequency,
and polarization, can be easily estimated using the power-law relationship as shown in
Equation (7):
γ = aRbp
(7)
where R p denotes rainfall rate exceeded at p% of the time, a and b are the functions of
frequency that depends on the polarization, which can be empirical values and can be
obtained experimentally [21,63]. The ITU-R P.838-3 [44] includes a look-up table for values
of a and b for frequencies ranging from 1 to 100 GHz in vertical and horizontal polarization.
3.3. Different Sources and Procedures of Rain Rate Data Collection
Due to the dependence of rain attenuation on rain data, available procedures for rain
data collection are defined in this section. These methods range from available databases,
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experimental, synthetic, and data-logged methods, to prediction techniques based on
interpolation methods.
3.3.1. Rain Data from Databases
The ITU-R Study Group 3 databanks (DBSG3) [64] rain database is one of the most
extensively utilized databases as it contains an extensive set of measurement data of attenuation due to various weather conditions. Furthermore, several weather databases from
European institutions, such as the European Center for Medium-Range Weather Forecasts,
are available and are alternative sources of rain rate data. Unfortunately, the centers do
not give information or have rain attenuation equipment for tropical locations. It has
therefore been established that those tropical countries need models that could assist in
developing their databases for rain data which could be used to prepare the corresponding
rain attenuation databases. Other databanks hold weather data that are local to their location; for example, in Nigeria there is the Nigerian Meteorological Agency (NiMet) that can
provide recent weather data that can be used by researchers to easily develop and evaluate
models as well as estimate the effect of these weather conditions on communication.
3.3.2. Synthetic and Data-Logged Method of Rain Rate Estimation
A mathematical method that can be used to generate rain attenuation time series
accurately, known as the Synthetic Storm Technique (SST), converts a rain rate time series
at a specific location into a rain attenuation time series [65]. This technique is used in place
of the logged data technique to save time and cost.
3.3.3. Experimental Setup
The best way to obtain the rain rate is by measurement through weather instruments
and facilities, for instance, a disdrometer or a rain gauge, at a reduced integrating time.
A disdrometer is a device that detects raindrop size distributions (DSD). In some instances,
the terminal velocity of falling hydrometeors can also be used to distinguish between various kinds of precipitation such as raindrops, snowflakes, graupel, or hail over time [66,67].
Figure 4 depicts a typical setup for rain rate measurement using a disdrometer.
Figure 4. Measurement System for a Disdrometer [68].
Many studies have employed various kinds of disdrometers, such as the JW RD80 disdrometer [69], or optical disdrometers which use image or laser technology [70,71],
to obtain rainfall data used in predicting the rain attenuation within a particular region.
However, they are subject to wind and evaporation errors.
The amount of rainfall within a particular location over a period of time can be
measured accurately using a meteorological instrument known as a rain gauge. Rain
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gauges are frequently utilized because of their ease of use and dependability, thus lowering
installation and maintenance costs. They also provide reliable in-place observations [66].
However, due to their sparse distribution, rain gauges are inadequate for estimating
area rainfall, especially in areas with many spatial variabilities, like mountain ranges.
The tipping bucket rain gauge is a popular rain gauge in which each tip correlates to a
specific amount of rainfall [72]. Figure 5 shows a tipping bucket rain gauge.
Figure 5. Tipping Bucket Rain Gauge [73].
Atmospheric parameters—air pressure, humidity, temperature, wind direction, and speed,
as well as precipitation—can be measured using instruments and equipment housed in
a facility known as a weather station. Information obtained can be employed to study
and forecast weather and climate. A weather radar is a remote sensing device used
by hydrological and meteorological communities to estimate area precipitation with high
spatial and temporal precision [74]. To measure the rain rate in some instances, the rain cell’s
radar information can be used by sending out an electromagnetic signal that interacts with
the raindrops at the atmospheric level, which would reflect the intercepted power towards
the radar—backscattering. According to [75], using radar to compute the rain rate primarily
involves three steps: (1) Calibrating the radar, which influences the accuracy of the rain
rates as a miscalibration can lead to bias in the rain rate results. (2) Quality control, which is
the scrutinization of the radar data to reduce the effects of non-meteorological scatters both
on the ground and in the atmosphere such as aircraft, etc. (3) Rain rate estimation using
the calibrated reflectivity values, which describes the size, shape, state, and concentration
of the hydrometeor as well as the azimuth, distance, polarization, intensity, phase, etc.,
which can be used to then compute the rain rate. Most research did not utilize a weather
radar, but rather used either a disdrometer or a rain gauge and, in most cases, used the
combination of both to get rainfall data.
3.3.4. Spatial Interpolation Method for Rain Rate Prediction
The use of spatial distribution methods for rain rate prediction is critical due to the
impossibility of measuring rain rates everywhere in a given location. When experimental
methods cannot accurately calculate rain rates, the spatial distribution method is employed
to ensure accuracy. Some mathematical models have been developed to improve the
accuracy of this method. One such model is called the Inverse Distance Weighting (IDW)
method, and the expression for the determination of the rain rate at a location up to 30 km
is as shown in Equation (8):
N
Rr =
∑ wi R i
i =1
(8)
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where Rr is the rain rate, N is the number of rain gauges, Ri is the weighted sum of the
rain gauges’ readings, and wi is the weight of each rain gauge reading. The MultiEXCELL
method can be used in a situation where local rain data are available. Several kinds of
literature have used this method to obtain rain rates. The calculation and estimation of
rain attenuation based on the three factors have been discussed in this section. The four
different data collection methods were also presented and discussed, as well as the spatial
interpolation methods of rain rate prediction used to ensure accuracy, based on mathematical models, in locations where an experimental setup cannot accurately measure the
rain rate.
3.4. Survey across Regions
Rain as a natural event is defined using different rate intensity thresholds. The most
generally used term in the literature is based on the interval when the rain rate exceeds
0.2 mm/h. Based on an approach that seeks to exploit the inhomogeneous nature of
tropical rain distribution, called Site Diversity, a rainfall event can either be convective
(CV), stratiform (ST), or storm-wind [76].
A CV rain event is an intense rainstorm within a small geographical area for a short
duration. ST, on the other hand, is a mild shower that lasts longer and is more widespread.
Storm-wind rain is a rainstorm characterized by intense clouds and severe aftereffects
in some local locations for brief periods [77,78]. Hence, since all the rain types are defined within the distance domain called rain cells, any two links located at different
places (or cells) will experience different levels of attenuation while receiving signals
from the same source [79]. CV and ST are more prevalent in the tropics and in temperate
regions, respectively [80].
Rainfall is a crucial climatic component in a tropical environment like Nigeria where
it can severely influence both earth-space and satellite communication links operating at
frequencies exceeding 10 GHz, thus making it a critical design factor for wireless communication systems. This impairment is termed rain attenuation and is found to vary
directly with both the raindrop size and the rain rate [81]. The two existing approaches for
estimating rain attenuation both use measured rainfall rate statistics, namely, (1) empirical
methods where attenuation due to rain is estimated using real measured rain data from
databases across various tropical areas, and (2) physical methods which deal with the
physical characteristics involved in the estimation of attenuation process [54,82]. However,
the resource implications for an empirical approach to be adopted, particularly in the
tropics, have rendered it less realistic, significantly impacting the availability of the muchneeded rain measurement data to build appropriate physical models for the design of
abundant wireless channels [83].
Over the years, the ITU-R sector has been able to develop, through research efforts,
a unified global model that can be used to estimate the attenuation due to rain for both LOS
and NLOS environments corresponding to the major global divide that has divided the
world into temperate and tropical regions. The new ITU-R 530-16 results from ongoing work
and developments to solve performance problems associated with prior models. However,
measured rain data from the equatorial and tropical areas have not been employed to
validate this model [84]. Table 4 shows the summary of different work carried out across
regions based on the ITU, including the model proposed, findings, and site locations.
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Table 4. Summary of Rain Attenuation across Regions.
ITU
Region
Country
Ref.
Model
Remarks
Location
Millimeter-Wave Propagation
model (MPM), ITU-R frequency
scaling model
Data on radiometric measurements
were presented in this study for
atmospheric attenuation at a
tropical location, demonstrating
that water vapor, as well as rain
rate, is an important cause of
attenuation at Ka-band frequencies.
Kolkata/Tropical
location
Salonen model
The obtained cumulative
distribution of liquid water content
deviates from ITU-R. The ITU-R
model eventually overestimates
cloud attenuation at a frequency
below 50 GHz and underestimates
at a frequency above 70 GHz.
Kolkata/Tropical
location, India
[87]
Raindrop size distribution in five
different locations in India
was assumed to be
lognormal distribution.
The dependency of the DSD on
climatic conditions leads to
attenuation disparity and
indifferent location between ITR-R
and DSD models.
Shillong (SHL),
Ahmedabad (AHM),
Trivandrum (TVM) for
three years each,
Kharagpur (KGP),
and Hassan (HAS) for
2 years each
[88]
The ITU-R model was evaluated
against the frequency diversity
model. Also, a higher fade margin
is used from 12 dB to 16 dB.
The developed model can minimize
signal attenuation in heavy rainfall
areas. Furthermore, the model is
suitable for higher fade margins.
Southeast Asia
[62]
The Abdul Rahman model, ITU-R
model, modified Silva Mello
model, modified Moupfouma
model, and Lin model were
compared for a horizontal
variation of rainfall.
The results showed that all models
estimated attenuation at 1 and
11 dB for both 6 and 28 GHz.
Johor Bahru, Malaysia
[89]
The method for converting
rainfall data is suitable for
satellite applications. The study
utilized three prediction models.
The predicted results were good
compared to direct observations
and other tropical conditions.
4-year data were
collected at UTM,
Skudai Campus,
Malaysia
[90]
77 locations are used to determine
the best fade margin for 5 GHz.
ITU-R P.837-7, ITU-R P.530-17,
and synthetic techniques were
employed to get 1-min data and
long-term rain attenuation.
The fade margin for 26 GHz is
obtained to be 6.50 to 10 dB for
99.99 link network availability.
However, at 28 GHz, the fade
margin was determined to be
7 to 11 dB.
Peninsular Malaysia
[91]
At 26 GHz, and distances of
0.3 and 1.3 km, the path reduction
factor was compared using the
ITU-R P-530-17, Abdulrahman,
Lin, and da Silva Mello models.
The results obtained have shown
that all the models accurately
predicted the attenuation due to
rain at 1.3 km.
Johor Bahru city
in Malaysia
Numerical Method
In the Ka-band, rain attenuation
and rain attenuation ratios
outperform the ITU-R model
in China.
58 locations in China
[85]
India
Asia
Region 3
Malaysia
[86]
Asia
Region 3
China
[92]
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Table 4. Cont.
ITU
Region
Country
China
Ref.
Model
Remarks
Location
The cumulative lognormal and
Gamma distributions of rain rates
were compared to half-empirical
conversion coefficients for China.
The study derives 1-min
cumulative distributions from
piecewise regression to a Gamma
distribution through half-empirical
conversion coefficients. It further
compares the two distributions and
concludes that Gamma
outperformed the datasets.
Hourly rain rates from
333 rain gauge stations
in China were taken in
1991 to study the point
rain rate cumulative
distributions
The study evaluated the
performance of each of the
following models: Abdulrahman,
ITU-R P.530-16, Mello,
Moupfouma models, Lin and
differential equation.
The research proposed new
prediction models based on the
correlation between the theoretical
and effective specific attenuation
validated by employing two links at
38 and 75 GHz. The study also
presented a 1-min rainfall rate
derivation from higher
integration time.
South Korea
[95]
40-year data for 22 locations
spread across the entire country
were used in the study.
Hybridization of Chebil and
refined Moupfouma-Martin
methods using data collected for
40 years across 22 locations.
The contour map of the rain rate
and attenuation for Ka and Ku
bands was developed using the
Kriging interpolation technique at
0.1 and 0.01%. The maps indicated
a higher rain rate in certain zones
than the ITU estimates.
Tanzania’s locations
include the Central
Area, Lake Victoria
basin, Northern Coast
including the Unguja
and Pemba Islands,
Northern Highland,
Southern Coast, South
Highland,
South-western,
and Western Area
[96]
A backpropagation neural
network (BPNN) was used for
dynamic rain fading. Markov
model was used to determine
storms’ frequency of occurrence,
and rain spikes for different
rainstorms were analyzed using
queueing theory.
The results have shown that the
maximum rain rate in Rwanda
ranges from 150 mm/hr and above
with an 11.42% probability of
occurrence. In addition, rain size
diameter is critical in rain
mitigation strategy development.
Butare (2.6078◦ S,
29.7368◦ E)
[97]
The specific attenuation for both
polarizations and the frequency
ranging from 1 to 100 GHz was
predicted using two years of
experimental data. The ITU-R is a
standard for rain attenuation.
According to the report, the western
part of Kenya is more vulnerable to
rain-induced network failures than
the rest of the country. It also
demonstrates that the horizontally
polarized radio wave is weaker
than its vertical counterpart.
Muranga, Kamusinga,
Mukumu, Kebabii,
and Habasweni,
Kenya
ITU-R
Findings from this research can be
applied to network planning in
South Africa for wireless networks
such as microwave and millimeter
broadband. The study
demonstrates that rainfall
attenuates terrestrial and satellite
LOS connectivity in the SHF and
EHF bands.
Data were collected at
one-minute intervals
over 2 years at 139.7 m
above sea level by the
Department of
Electrical and
Electronics and
Computer Engineering
of the University of
KwaZulu-Natal
[93]
Asia
Region 3
Korea
Tanzania
[94]
Africa
Region 1
Rwanda
Kenya
Africa
Region 1
South
Africa
[98]
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Table 4. Cont.
ITU
Region
Country
Ref.
Model
Remarks
Location
[99]
ITU-R, Kriging Interpolation
method. One-minute rain rate
and rain attenuation contour
maps models were developed for
the selected locations.
The study provides useful results
for terrestrial and satellite system
designers to determine the
appropriate EIRP and receiver
point characteristics over the
desired coverage area.
Eastern Cape, Free
State, Gauteng,
Kwazulu-Natal,
Limpopo,
Mpumalanga,
Northwest, Northern
Cape, Western Cape
A rain rate model has been
developed suitable for
10 locations in South Africa,
compared to the model proposed
by ITU using a power-law
regression model.
The research was carried out at
three different frequencies: 12, 30,
and 60 GHz for 30-sec, 1-min,
and 5-min rain rates.
The developed model provided
detailed information on the specific
attenuation for both micro- and
millimeter-wave frequencies in
South Africa.
The study locations are
Upington, Polokwane,
Mossel Bay, Mafikeng,
Irin, East London,
Durban, Cape Town,
Bloemfontein,
and Bethlehem
The Mie scattering approach was
utilized to predict specific rain
attenuation, and many
distributions, such as log-normal,
were employed to
forecast attenuation.
The results reveal that the
extinction coefficients are more
temperature dependent at lower
frequencies for the lognormal
distribution. Furthermore, at lower
microwave frequencies,
the absorption coefficient is high
but declines exponentially with
rain temperature.
4 diverse locations
in Botswana
A 12-year experimental rainfall
dataset was employed to develop
a realistic predictive model for
rain rate intensity levels
was performed.
Results showed that horizontal
polarization has a 12% higher rain
attenuation than
vertical polarization.
Lokaja, Kogi State,
Nigeria
[34]
Empirical attenuation model
based on prognosis for
earth-space communication
frequency in a tropical savanna
climate region.
The results indicated a consistent
increase in the attenuation as the
signal frequency increased where
free space is more prevalent.
The results also demonstrated that
the effect of clouds and gases on
signals is less when
compared to rain.
Lokaja, Kogi State,
Nigeria
[101]
The Moupfouma and ITU-R
models for Kumasi were
evaluated against the local 1-min
measured. The inverse-distance
weighting method and Arc GIS
software were used to develop
geographical maps.
The results from this study were
employed to choose a best-suited
estimation model for the 22 weather
stations in Ghana. After that,
the ITU-R model estimated the
attenuation due to rain.
Kumasi, Ghana
[102]
Two ITU-R—P.530 and
P.838—standards were used to
calculate the losses in 5 GHz with
99.9% link availability at 24 GHz,
28 GHz, and 38 GHz.
Attenuation is proportional to the
rainfall rate, frequency,
and polarization.
Palo Alto, California
South
Africa
[69]
Africa
Region 1
Botswana [100]
[33]
Nigeria
Africa
Region 1
North
America
Region 2
Ghana
USA
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Table 4. Cont.
ITU
Region
Country
Ref.
[103]
Model
Remarks
Location
Power-law rain estimation model,
global rain attenuation
prediction models
The research proposed a rain
estimation model for the S-band
based on observations in a
specialized and precise
experimental setup, revealing that
rain attenuation is non-negligible at
frequencies above 6 GHz.
Ioannina,
northwestern Greece
ITU-R P.618-9, ITU-R P.838-3,
and ITU-R P.838-3, respectively,
for Attenuation, Specific
Attenuation, and Rain Height.
Six-year point rain data collected by
the National Hellenic
Meteorological Service (NHMS)
was employed to derive statistics
for 0.001%-time of the average year,
which were then used to create
precise maps of rain rate and
attenuation to aid in the design of
Greece’s satellite
communications systems.
12 locations were used
in the study: Agrinio,
Alexandroupoli,
Hellinikon, Heraklion,
Ioannina, Lamia,
Larisa, Limnos, Milos,
Pyrgos, Serres,
and Chios
Tropical rain measuring mission
satellite to determine the rain rate
distribution in the tropics.
The results have shown how the
rain rate over 5 km was converted
into 1 km square with the help of a
correction factor. Finally, the results
were compared with ITU-R
DBSG3 and Ref. ITU-R P. 837-7.
42 sites were used in
the studies
Greece
Europe
Region 2
[104]
UK
[105]
4. Rain Attenuation Models
In this section, the different existing rain attenuation models classified under five
categories are presented and discussed as well as a brief review of previous research on
rain attenuation in 5G using these models.
4.1. Empirical Models
This section discusses some of the empirical models and reviews relevant work on
rain attenuation for 5G using these models. An empirical model is based on experimental
data observations that can be described mathematically. Eight empirical models are used
for the rain attenuation model, and a review of the rain attenuation modeling using these
models is presented in Table 5.
4.1.1. Garcia Model
This model [106] is one of the modified versions of the Lin model that is best suited for
temperate European locations and can be represented mathematically expressed as shown
in Equation (9):
1
A = aRrb L p
0.5 +
L(3Rr −3.9L p +255)
2636
, for Rr > 10 mm/h, L p > 5 km
(9)
where A denotes rain attenuation (dB), Rr denotes rain rate (mm/h) averaged on a given
time interval of 1 min, L p is the path length in km, and a and b are functions of frequency.
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4.1.2. Crane Model
This model [37] predicts high attenuation in low rainfall locations and offers global
rain distribution. It can be expressed mathematically as shown in Equation (10):
#
"
uαd − 1
α ecαd
α ecαD
b
b
e
, d ≤ D ≤ 22.5km
(10)
−
+
A = aRbp
uα
cα
cα
where A denotes rain attenuation (dB), R p denotes rain rate (mm/h) exceeded at %p of the
time, and a and b are functions of frequency. Other remaining coefficients are empirical
constants of the model, expressed in Equations (11)–(14):
h
i
ln becd
,
(11)
u=
d
0.17
b = 2.3R−
,
p
(12)
c = 0.026 − 0.03 ln R p
(13)
d = 3.8 − 0.6 ln R p
(14)
4.1.3. Mello Model
This model [40] was developed by utilizing a complete rainfall rate distribution as
input to predict the rain attenuation cumulative distribution due to the inaccurate prediction
of the ITU-R model, where two regions having different rainfall rate conditions would have
similar values of rain attenuation A p , and can be expressed mathematically as shown in
Equation (15):
h
ib
Lp
A p = a 1.763Rr 0.753+0.197/L p
(15)
1 + L p /119Rr −0.244
where A p denotes rain attenuation exceeded at p% of the time, Rr denotes rainfall rate in
mm/h, L p is the path length, while a and b are functions of frequency.
4.1.4. Moupfouma Model
In this model [39], L T is the distance between ground stations, that is, the actual
propagation path length; its equivalent propagation path length Leq can be determined
using an adjustment factor that ensures the uniformity of the rain on the entire propagation
path and can be represented mathematically as shown in Equation (16):
A p = aRbp × Leq R p , L T
(16)
where A p is the rain attenuation expressed in dB exceeded at p% of the time. The specific
attenuation in terms of the rain rate expressed in dB/km is aRbp , R p is the rain rate exceeded
p% of the time, and Leq is the equivalent path length for which the rain propagation is
assumed to be uniform.
4.1.5. Peric Model
According to [47], this dynamic model has no real network–environment test and
application. Rather, it is based on the cumulative distribution function for a particular area
of interest, the number of rain occurrences that exceed the rain intensity threshold, the rain
advection vector intensity, and the rain advection vector azimuth.
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4.1.6. Abdulrahman Model
Using a variety of non-linear regression approaches, this model [107] investigates the
correlation between path adjustment factors and different physical path lengths. The rain attenuation, based on this model, can be expressed mathematically as shown in Equation (17):
Ap = µ S Rp
(17)
where:
µ=
"
Rp
α + b 1 − rp
#
(18)
at p% of the time, R p denotes rain rate (mm/h)
A p is the rain attenuation (dB) exceeded
exceeded at p% of the time, and S R p is the slope which can be expressed in Equation (19):
where:
4.1.7. Da Silva Model
S R p = βRαp−1
(19)
β = k α + b 1 − r p Le
(20)
This model [108] was primarily developed to estimate the rain attenuation in earth–
space and terrestrial links. The model utilized a complete rainfall rate distribution as input
and can be applied for terrestrial and slant links. A more general prediction method that includes slant links but is more suitable for terrestrial links can be represented mathematically
as shown in Equation (21):
b
A p = a Re R p , L p , θ
.
Lp
1 + L p cosθ/Lo
(21)
where A p denotes rain attenuation exceeded at p% of time, R p denotes rain rate (mm/h)
exceeded at p% of time, a and b are functions of frequency, Re denotes the approximate
effective rain rate and can each be expressed mathematically as shown in Equation (22):
Re = 1.74Rr 0.786+0.197/L p cosθ
(22)
For slant links, L p = (hr − h a )/ sin θ. However, for terrestrial links, θ = 0 and Lo = dc ,
which denotes cell diameter expressed as shown in Equation (23):
dc = 119Rr −0.33
(23)
4.1.8. Budalal Model
This model [43] is best suited for short-range outdoor links in a 5G network with frequencies above 25 GHz. It can be expressed mathematically as shown in Equations (24) and (25):
"
#
1
Ifγ =
, for f ≤ 40 GHz, L p < 1 km
(24)
0.05
1.77L p 0.77 R−
p
Ifγ =
"
1
0.633
0.477L p
R0.073
f 0.123
p
#2
, for f > 40 GHz, L p < 1 km
(25)
where L p is the path length, R p denotes rain rate (mm/h) exceeded at p% of time, f is the
frequency in GHz, and I f γ is the proposed Increment Factor.
Table 5 presents the summary of works that have utilized these empirical models
including the methodology adopted, method of validation, and findings.
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Table 5. Summary of Rain Attenuation Using Empirical Models.
Ref.
Objective of Research
Methodology Adopted
Method of Validation
Result Obtained
Year
[43]
To investigate and
modify the ITU-R
P.530-17 rain attenuation
prediction model for
terrestrial line-of-sight at
short-distance for 26 and
38 GHz at mm-wave
frequencies in Malaysia.
Two links operating at
26 and 38 GHz were
used to collect weather
data employing a
Casella rain gauge for
1 year with a 1-min
integration time and
path length of 300 m.
The study validated the proposed
model by employing two links
operating at 25 GHz with a path
length of 223 m in Japan and
75 GHz with a path length of
100 m in Korea.
The results reveal that
all estimations are
close to the suggested
prediction model.
2020
[2]
To compare five
different prediction
models to find the
optimal rain attenuation
model for 5G
in Malaysia.
The research utilized a
1-year precipitation data
collected from a tipping
bucket rain gauge over a
link of 0.2 km path
length operating at
6 and 28 GHz.
The rain attenuation was
calculated from the product of the
specific attenuation and the path
length at a rain rate of 0.01% as
shown below:
A p = γLeq
Results revealed that
the modified Mello
model estimated a
lower value for the
attenuation for low
and high operating
frequencies.
2020
The results showed
that the ITU-R model
overestimates the
attenuation for lower
rain rates, whereas for
higher rain rates it
estimates lower
attenuation than the
DSD model.
2019
A power law equation was used
to calculate the expected signal
attenuation (theoretical) and then
compared with the measured
signal attenuation (practical):
A = aRb
Results showed that
the measured signal
attenuation was
1–1.5 dB greater than
expected for 28 GHz,
1.6–2.5 dB greater than
expected for 38 GHz
due to the wet antenna,
and a signal loss of
4.2 dB was recorded
over the 700 m link.
2019
Two ITU-R models—P.530-16 and
P.838-3—were employed to
measure the effect of rain on the
propagation of
electromagnetic signals.
Results showed that
the specific
attenuation at 0.01%
was 26.2 dB/km at
120 mm/hr. The rain
rate and the estimated
rain attenuation across
1.3 km was 34 dB.
2018
The results
demonstrated that the
locally determined
power laws appear to
be the most accurate
link between specific
attenuation and
rainfall intensity.
2017
[20]
[21]
[62]
[109]
To investigate the effect
of rain on short-range
fixed links, that is,
building-to-building
transmission.
Data utilized for this
research were obtained
using a PWS100 highperformance
disdrometer at 25.84 and
77.52 GHz.
To investigate the effect
of precipitation and wet
antennas on
millimeter-wave
transmission links
operating at 28 GHz and
38 GHz Line of Sight
(LOS).
The study used a 700 m
path length millimeter
wave link operating at
28 and 38 GHz in central
Beijing. A disdrometer
and rain gauge was
employed to measure
rainfall and the received
signal level was
gathered every 15 s.
To investigate the effect
of rain using real-world
observations on
mm-wave propagation
at 26 GHz frequency.
The study used a
microwave 5G radio link
technology with a
1.3 km path length to
collect measurements
logged daily. Then,
every year, MATLAB
was utilized to process
and analyze data.
Improve rain
attenuation estimates for
5G wireless networks
operating in heavy rain
zones at 28 GHz and
38 GHz.
The study used 3-year
raindrop size
distribution data
gathered in Kuala
Lumpur, Malaysia,
utilizing a “Joss-type”
RD69 disdrometer,
which comprised
100,512 rainy data with a
1-min integration time.
The ITU-R and DSD models were
employed to predict the
attenuation due to rain expressed
mathematically as:
γ = aRb
R ∞γ =
4.343 × 103 0 δext ( D ) N ( D )dD
Gamma and normalized models
were used to evaluate the
performance and can respectively
be expressed mathematically as:
N ( D ) = No D µ e−(∧ D)
N (D) =
D
µ
Nw f (µ)( DDm ) e[−(4+µ) Dm ]
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Table 5. Cont.
Ref.
[94]
[19]
Objective of Research
Methodology Adopted
To compare six
alternative models to
find the best rain
attenuation model for
higher microwave bands
in Icheon, South Korea.
The study used 3-year
rainfall data gathered
via line-of-sight
terrestrial links at 38 and
75 GHz, with path
lengths of 3.2 and
0.1 km, respectively,
and an average
sampling rate of 1 min.
To investigate the impact
of rain on short-range
radio networks
operating at the 35 GHz
mmWave frequency.
The measurement was
experimentally obtained
from a 35 GHz radio link
with a path length of
230 m to measure
rain-specific attenuation
and rain rate
distribution.
Method of Validation
The relative error margin, ε p , was
employed to evaluate the models
and can be expressed
mathematically as:
εp =
R pred ( P)− Rmeas ( P)
Rmeas ( P)
× 100%
Experiments of rain attenuation at
103 GHz with a path length of
390 m at different rainfall rates
were conducted to validate the
rain rate distribution.
Result Obtained
Year
The analytical results
showed that the ITU-R
P. 530-16 model
predicted accurately
for both 38 and
75 GHz, whereas the
Abdulrahman model
predicted accurately
for just 38 GHz.
2017
Results after
comparison showed
that Wellbul
distribution for
raindrops is following
the experiments.
2006
4.2. Statistical Models
This section introduces and discusses the various prediction models in the statistical
model category and reviews previous works based on these models on rain attenuation
for the 5G network. A statistical model, as opposed to an empirical model, is based on
statistical meteorological data analysis, and results are derived by regression analysis.
Two statistical models are considered: the ITU-R model and the Singh model.
4.2.1. ITU-R Model
This model can estimate rain attenuation for frequencies ranging from 1 to 100 GHz
with path lengths up to 60 km. It is based on the distance factor that depends on the
rain-rate Rr , link length, frequency, and the coefficient of the specific attenuation γ [47,110].
The International Telecommunication Union’s Radiocommunication Sector (ITU-R) issued
some recommendations that have become the most generally used globally in estimating
rain attenuation [63]. The ITU model is based on a parameter of 0.01% of the annual
rain rate. Rain attenuation is caused by the overall rainfall crossing the propagation path,
typically described as the integration of the specific attenuation along the path. The model
gives 99.99% fade depth attenuation as expressed in Equation (26):
A p = aRrb dr (dB)
(26)
where Rr is the rain rate measured in mm/h and defined as 99.99% of the rain rate for a
specific location, aRb is measured in dB/km and gives the specific attenuation, L p is the
length of the link measured in km, a and b are functions of frequency at 20◦ C, while path
adjustment factor r is expressed as in Equation (27):
r=
1
1 + L p /Leq
(27)
where Leq gives the effective path length and is mathematically expressed as shown in
Equation (28):
Leq = 35e−0.015Rr (km)
(28)
The model has become a worldwide baseline for evaluating research findings, though
not without flaws, such as focusing on the effect of rain while ignoring the effects of
other meteorological elements such as snowflakes or hail [84]. The model has further been
reported to indicate a poor correlation with experimental data, especially in the tropics [111].
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Furthermore, its use outside its prescribed limited frequency and rain rate ranges could
inflict up to a whopping 10% error [112]. It also features more complex computations
involving high-frequency asymptotic expansion because of the inhomogeneous nature of
tropical raindrop-size distributions [113–115]. Lastly, for wider applications, the model
depends on extrapolation in respect of computations for rain spheres and rain rates, which
could be a potent source of error in the tropics [116]. Because of the above limitations
of the ITU-R P618-9, the latest modification—ITU-R P530-16—features the inclusion of
location-tuning parameters [84]. The path reduction factor can be expressed as given in
Equation (29):
r=
0.477
L0.633
p
R0.073a
f 0.123
p
1
− 10.579 1 − exp −0.024L p
(29)
The equations of interpolation for various percentages of time ranging from 0.001 to
1% are expressed in Equations (30)–(34):
Ap
= C1 P−|C2 +C3 . log10 P|
A0.01
h
i
C1 = 0.07C0 0.12(1−C0 )
C2 = 0.855C0 + 0.546(1 − C0 )
C0 =
C3 = 0.139C0 + 0.043(1 − C0 )
0.8
f
f ≥ 10 GHz
0.12 + 0.4 log10 10
0.12
(30)
(31)
(32)
(33)
(34)
f < 10 GHz
where R p denotes the rain rate (mm/h) exceeded at p% of the time, r denotes the path
adjustment factor exceeded at the same percentage of the time, L p denotes the radio path
length (km), Cn denotes the interpolation constant where n = 1,2,3, while a and b are
functions of frequency obtained from [44]. The latest modification to the ITU-R model
line, the ITU-R 530-16, has been reported to have shown significant improvement in
handling attenuation, even though point accuracy is still far-fetched, and there is a need
for more sustained efforts on a more realistic estimation of attenuation in the tropical and
equatorial regions.
4.2.2. Singh Model
For the frequency range of 1 GHz to 100 GHz, the Singh model adopts the analytical
method of ITU to determine specific attenuation, depending on the polarization type,
vertical or horizontal. However, for most of the computational system requirements,
the Singh model is simpler than the ITU model as it tries to do away with the requirement of
determining the frequency-dependent regression coefficients, a and b. Due to the intricacy
of the other prediction models, this is a simple mathematical model that has only square
and cubic equations that are solely reliant on the frequency and rain rates. As a result,
calculating the attenuation induced by higher frequencies at any given frequency and rain
rate is relatively simple [63]. The mathematical representation of this model is given in
Equation (35):
γ = wf3 + xf2 + yf + z
(35)
where γ denotes the specific attenuation (dB/km), f denotes the frequency, and the coefficients w, x, y, z for horizontal polarization in terms of the rain rate Rr are given in
Equations (36)–(39):
wh = 1.422 × 10−9 R2r + 2.03 × 10−7 Rr − 1.21
(36)
xh = 1.963 × 10−7 R2r + 8.618 × 10−7 Rr + 0.0019
(37)
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yh = 2.114 × 10−6 R2r + 0.01Rr − 0.036
zh =
3 × 10−5 R2r
(38)
− 0.040Rr –0.031
(39)
and Equations (40)–(43) are for vertical polarization:
wv = −5.520 × 10−12 R3r + 3.26 × 10−9 R2r − 1.21Rr × 10−7 − 6 × 10−6
(40)
xv = 8 × 10−10 R3r − 4.552 × 10−7 R2r − 3.03Rr × 10 −5 + 0.001
(41)
yv =
(42)
−5.71 × 10−9 R3r
+ 6 × 10−7 R2r
+ 8.707Rr × 10
−3
− 0.018
zv = −1.073 × 10−7 R3r + 1.068 × 10−4 R2r − 0.0598Rr + 0.0442
(43)
Table 6 presents the summary of works that utilized these statistical models including
the methodology adopted, method of validation, and results obtained.
Table 6. Summary of Rain Attenuation Using Statistical Models.
Objective of
Research
Methodology Adopted
Method of Validation
[117]
To study rain
attenuation for
mmWave 5G
applications utilizing
long-term statistics
across short-range
fixed networks.
This study utilized
3 different experimental
links to collect
precipitation data.
The first two links have
a path length of 36 m
operating at 25.84 and
77.54 GHz, while the
third link has a path
length of 200 m
operating at 77.125 GHz.
The ITU-R and DSD models were
employed to predict the
attenuation due to rain expressed
mathematically as:
γ = aRb
R ∞γ =
4.343 × 103 0 δext ( D ) N ( D )dD
[72]
To extensively
provide analysis on
the 1-min rain rate
and attenuation
forecast for 5G
communication links
by evaluating
rainfall data at
26 GHz and 38 GHz
propagation
frequencies.
[118]
To study the
effectiveness of
several ITU models
in predicting rain
rates and attenuation
in Malaysia’s
tropical climate with
the worst month
parameter
estimation.
[91]
To evaluate the path
adjustment factor of
the ITU-R,
Abdul-Rahman, Lin,
and Mello models
for rain attenuation
estimation.
Ref.
Result Obtained
Year
The investigation revealed
that the DSD required
more than rainfall rates to
estimate attenuation
effectively, but the ITU-R
P.530-18 performs better
with a limited
distance factor.
2022
ITU-R P530-17 model was used to
evaluate the rain rate at 0.01%
and can be represented
mathematically as:
A p = γLeq
Results showed that
attenuation is directly
proportional to both
frequency and
pathlengths; therefore,
there would be a
high-value attenuation for
a path length above 1 km.
Hence, there is a need to
increase the output power
above the computed
attenuation value.
2021
The study used three
datasets from various
times and locations in
Malaysia that were
collected over a
Line-of-Sight scenario at
26 GHz and 1.3 km.
The work measured the
performance of ITU models.
The study utilized the absolute
error at 0.01% and Root Mean
Square Error (RMSE) model
validation techniques.
Results showed that
ITU-R 837-1 is more
appropriate than other
ITU-R models in
predicting climate
properties based on the
absolute error and the
computed RSME. ITU
method 2 outperforms
other ITU models.
2021
Two Ericsson links at
26 GHz and different
distances of 1.3 km and
0.3 km were employed
to collect data for two
years at a sample period
of one second.
The rain attenuation was
calculated from the product of the
specific attenuation and the path
length at a rain rate of 0.01%, as
shown below:
A p = γLeq
At a path length of 0.3 km,
none of the models
successfully predicted rain
attenuation; however, at
1.3 km, all models
accurately estimated the
rain attenuation.
2020
The study used a
tipping bucket rain
gauge connected to a
data logger to collect
2-year rainfall data at
the Bosso campus of the
Federal University of
Technology in Minna.
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Table 6. Cont.
Ref.
Objective of
Research
Methodology Adopted
Method of Validation
Result Obtained
Year
2020
[119]
To evaluate the
statistics of the
attenuation due to
rain for 5G in heavy
rain zones of
equatorial Malaysia.
The research utilized
3-year data collected
between the years
1992–1994 in Kuala
Lumpur, Malaysia,
with a 1-min
sampling interval.
ITU-R model and the synthetic
storm technique (SST) were
employed to estimate the
attenuation due to rain based on
varying path lengths, frequency,
and monsoon impacts.
The results showed that
for 0.2 km, the estimated
attenuation was less than
5 dB, implying that the
shorter the distance
between the base stations,
the smaller the influence
of rain attenuation,
therefore improving the
link’s performance.
[71]
To study rain
attenuation and its
relationship to
operational
frequency and Drop
Size Distribution
(DSD).
A laser-based
disdrometer was
employed to collect rain
data for 1 year over two
radio links of 325 m
operating at 73 and
83 GHz.
The work evaluates the accuracy
of the prediction model.
The measured data obtained from
both links were compared to an
ITU-R model.
Results showed that the
SC EXCELL and Lin
models accurately
estimate short links
irrespective of
the frequency.
2020
[22]
To study the
influence of the
attenuation due to
rain attenuation on
both direct LOS and
indirect NLOS side
links for
short-distance
building-to-building
transmission.
The study collected
weather and channel
data over two mmWave
bands using a
high-performance
PWS100 disdrometer
and a custom-made
channel sounder,
respectively, between
25.84 and 77.52 GHz.
The work utilized the rain rate,
and the attenuation due to rain
from both links was compared to
an ITU-R model and the DSD
model using Mie scattering.
The results show that the
indirect NLOS side link
experiences a greater
amount of attenuation
than the direct LOS link.
2020
[120]
To investigate the
effect of rainfall
intensity on radio
propagation at
21.8 and 73.5 GHz in
the K and E bands,
respectively.
Two E-band links at
73.5 GHz with distances
of 1.8 and 0.3 km and
one K-band 21.8 GHz
link at a distance of
1.8 km were utilized to
obtain data at a sample
interval of 15 min.
The empirical CDF for the highest
rain attenuation was evaluated
against the 1-min estimated rain
attenuation CDF and also with
some other prediction models.
The results obtained at a
rain rate of 140mm/hr
and time percentages of
0.03% and 0.01% showed
that the E-band has 10 dB
attenuation more than
the K-band.
2020
[110]
To determine which
rain attenuation
models, ITU-R
P.530-17 and Mello
and Ghiani’s model,
provide the accurate
estimation for 5G
networks in the
tropical environment
of Malaysia.
The research employed
two experimental
millimeter-wave links
running at 26 and
38 GHz, with a path
length of 301 m between
antennas, as well as a
data gathering system
and a sample
period of 1 s.
The results showed that
the ITU-R model gave the
closest estimation to the
measured attenuation;
hence, it is best suited for
tropical environments.
2019
To study the impact
of the attenuation
due to rain on
26 GHz mm-wave
signal in a wet
tropical location.
The study utilized a 5G
microwave link
operating at a 26.2 GHz
frequency band as a
test-bed with a path
length of 1.3 km to
continuously collect
measurements for one
year for a sampling
interval of 1 min.
The results showed that
the ITU-R model
inaccurately estimated the
rain rate and attenuation
up to a percentage value
of 143% and 159%,
respectively, for the
study area.
2019
[121]
The work validates the models.
The relative error figure ε p was
employed, which is
mathematically represented as:
εp =
Rest ( P)− Rmeas ( P)
Rmeas ( P)
× 100%
The linear regression method was
used to calculate the rain rate of
the worst month and the statistics
of the rain attenuation:
Q = Q1 p + β
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Table 6. Cont.
Objective of
Research
Methodology Adopted
[122]
To investigate the
rain attenuation
cumulative
distribution and
rainfall rate
in Ukraine.
The study utilized
rainfall data collected for
a time interval of 5 min
with a 15 dB/km
attenuation threshold for
a 1 km horizontal
channel operating at
frequencies 28, 38, 60,
and 94 GHz.
[123]
To investigate
various uplink and
downlink frequency
bands, the overall
atmospheric
absorption caused by
dry air (oxygen) and
water vapor on the
earth-space path.
Ref.
Method of Validation
Result Obtained
Year
Radio-physical MPM model was
used to determine the warm and
worst months of the year.
The results showed that
reliable communication at
zenith-range and average
angles of view is
attainable for all studied
frequency ranges with a
99.99% probability for a
1-year estimation term.
2019
This study utilized
7-year meteorological
data gathered from
Atmospheric Infrared
Sounder (AIRS)
satellites between the
years 2002 and 2009.
The Rec. ITU-RP 676 model was
utilized to validate the results.
The results showed that
there was 99.9%
availability in C and Ku
bands in West Africa with
low fading between
0.04–0.09 dB and
0.01–1 dB, respectively.
2018
[84]
To validate a new
ITU-R rain
attenuation
prediction model
over Malaysia’s
equatorial region.
The study utilized radar
and rain gauge data
obtained from MMD
and DIDM over six
different links in six
distinct locations.
The proposed model was
compared with four other rain
attenuation models in terms of
the RMS, standard deviation,
and mean error.
Results showed that the
new ITU-R model was
able to address the
problem of
underestimation faced by
the existing ITU-R model.
2019
[70]
To estimate the
rain-specific
attenuation of
horizontally and
vertically polarized
millimeter waves
using T-matrix
calculations.
A 2-dimensional video
disdrometer (DVD) was
used in this research to
collect 1-year rainfall
data across terrestrial
links operating at
38 GHz in Peninsular
Malaysia.
The power-law fit relationship
was used to compare the
estimated values from the 2-DVD
dataset with values from the
ITU-R P.838-3:
γ = aRrb
The results showed that
the power-law fit
excellently corresponds
with the local-laws fit.
However, there are
numerous inconsistencies
with the ITU-R
recommendation.
2017
[94]
To study how local
environment
propagation affects
the slant path
attenuation for both
Ku and Ka bands.
This research utilized
3-year rainfall data
collected using two
experimental setups
operating at dual-band
frequencies of 12.25 and
20.73 GHz and 6 and
19.8 GHz, respectively.
Two ITU-R models were
employed for analysis with
experimentally derived
coefficient sets.
The results demonstrated
the importance of the
regression coefficients for
specific attenuation based
on ITU-R
recommendations.
2017
[124]
To investigate rain
attenuation
estimation in both
millimeter and
microwave bands in
Ethiopia for
terrestrial radio
networks.
The study utilized
two-year rain intensity
data collected from
Ethiopia’s national
meteorological agency
with a 15-min
integration time for
various year
percentages.
The ITU-R model was also
employed to estimate the rainfall
attenuation for ten different sites
around the country over
terrestrial radio links.
According to the findings,
Bahirdar and Dubti are
expected to receive the
most and least amount of
rain attenuation,
respectively.
2015
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Table 6. Cont.
Objective of
Research
Methodology Adopted
Method of Validation
Result Obtained
Year
[125]
To study 1-min rain
rate information
collected over two
years in Akure,
Nigeria.
An electronic weather
station and a
self-emptying tipping
spoon were employed to
obtain measurements
and gather rain data
which were then stored
using a data logger.
The work validates the result,
the prediction error, RMSE,
SC-RMSE, as well as the
Spearman’s rank correlation
were employed.
Findings revealed that no
single model would
provide a decent fit while
outperforming all others.
2014
[126]
To highlight the
disparity between
recorded attenuation
due to rain for
tropical Malaysia as
well as ITU-R
projections.
Four links were
employed, each
operating at a different
frequency, 14.6, 21.95, 26,
and 38 GHz, with a path
length of 300 m and
a 1-min
integration period.
The rain attenuation estimation of
the ITU-R was evaluated against
the measured rain
attenuation CDF.
Results showed that the
path length is proportional
to the deviation, and the
ITU-R prediction model
was underestimated for
tropical regions.
2013
[81]
To analyze
one-minute rain data
collected in South
Africa from January
to December 2009.
The study measured
1-min rain data using a
JD RD-80 disdrometer
for a total period of
1 year to obtain 729 rain
rate samples.
The chi-squared statistics, as well
as the root, mean square tests,
were employed to validate the
results accurately.
The results showed that
the gamma model
performed the best for the
different classes of rain
taken under
consideration.
2011
To propose a
modified ITU-R rain
attenuation model in
tropical climates,
particularly
for Malaysia.
The research utilized
3 years of rain rate and
rain attenuation data
obtained from satellite
Super-C where
frequency, cumulative
rain rate, and elevation
angle were the
major parameters.
Comparison between the
proposed model and the existing
ITU-R model in terms of rain
prediction errors such as RMS
and percentage error.
Results showed that the
proposed model
performed better than the
existing ITU-R model;
hence, it is suitable for a
tropical climate
such as Malaysia.
2011
Ref.
[127]
4.3. Fade-Slope Model
This section discusses the different models in the fade-slope models category and
reviews previous works on rain attenuation for 5G networks using these models. The fade
slope represents the variation in the attenuation due to rain in terms of the attenuation level,
sample time, and environmental conditions such as drop size distribution and rain type.
To establish the fade mitigation measures, a fade slope is necessary. The two fade-slope
models discussed here are the Andrade model and the Chebil model.
4.3.1. Andrade Model
The fade-slope variance [128] is proportional to the attenuation and can be expressed
mathematically as shown in Equation (44):
f ( f s | A) = √
1.38
K· A[1 + f s2 /K· A]
6.7
(44)
where f s is the fade slope, A denotes the rain attenuation,
and K is the constant of pro
portionality. The next level attenuation A ti + t p can be estimated from the current
attenuation value A(ti ) and fade slope f s randomly by the predictor using Equation (45):
A ti + t p = A ( ti ) + f s t p
(45)
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where t p denotes the prediction time; t p = 10 can be considered the minimum prediction
time or the time of experimental sampling data.
4.3.2. Chebil Model
This model [129] can be expressed mathematically as given in Equation (46):
!2
1
f
s
√ exp0.5
P=
σfs
σ f s 2π
(46)
where P is the conditional distribution of the fade slope, f s is the fade slope, and σ f s is the
fade-slope standard deviation expressed in Equation (47):
σ f s = 0.00012A3 − 0.003A2 + 0.027A − 0.0016
(47)
where A is the rain attenuation.
Table 7 presents the summary of research works that have used these fade-slope
models including the methodology adopted, methods of validation, and results obtained.
Table 7. Summary of Rain Attenuation Using Fade-Slope Models.
Ref.
Objective of
Research
Methodology Adopted
Method of Validation
Result Obtained
Year
[130]
To investigates the
propagation of the
mm-waves at the
38 GHz link based
on real measurement
data in Malaysia.
The study used 1-year
rainfall data gathered
over a 38 GHz
line-of-sight link with a
path length of 300 m and
a sample interval
of 1 min.
The distributions of the
attenuation due to rain was
evaluated against the modified
ITU-R distance-factor model at
different time percentages to
validate the accuracy of
the model.
The results showed
excellent correspondence
between the modified
model’s estimation and
the measured rain fade in
Malaysia as well as other
available data from
various locations.
2022
[90]
To examine the
impact of
attenuation due to
rain for 5G in
Malaysia and
propose an optimal
rain fade margin.
A tipping bucket rain
gauge and
RD-69 disdrometer were
used to obtain three
separate datasets for
various periods.
The prediction model error, ε p ,
was used to validate the models
and is represented
mathematically as:
The results showed the
optimum attenuation
margin for 5G should
range from 6.5 to 10 dB for
a 26 GHz link and 7 to
11 dB at the 28 GHz link.
2021
To study the
properties of the
measured rain fade
slope distribution
for different
attenuation levels.
Three experimental
microwave links were
employed for this study
at 300 m.
To validate the model,
the chi-square goodness-of-fit test
was employed:
The results showed that
the ITU-R model
evaluated against relevant
measured distributions
could not be generalized
for all cases.
2020
To study and
evaluate fade slope
for rainstorms with
speeds greater than
40 mm/h at various
rain type
boundaries.
The study used 2-year
rain-rate data from
Durban, South Africa,
using a RD-80
disdrometer and a 30-s
sampling time.
The results revealed that
the fade slope is related to
the attenuation threshold
and is affected by the type
of rain.
2019
[129]
[131]
εp =
Rest ( P) − Rmeas ( P)
Rmeas ( P)
Xc2 = ∑iN=1
× 100%
(Oi − Ei )
Ei
2
The rate of change of attenuation,
R, and the attenuation threshold,
T, have a power-law relationship,
which is given by:
R = uT v
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Table 7. Cont.
Ref.
Objective of
Research
Methodology Adopted
Method of Validation
Result Obtained
Year
[21]
To study the effect of
rain intensity on
signal-level
measurements for
mm-wave radio
links across short
distances and
quantify the fading
bias to achieve a
more accurate
estimate of the rain
rate over the link.
LOS scenarios were
measured using
backhaul networks
during rainy days in
Beijing, China.
The reading was
compared to local rain
measurements from a
disdrometer and
a rain gauge.
At 25 GHz, a wet test revealed up
to 4 dB loss due to the thickness
of the water coating on the
antenna. After the rain ended
(one hour later), the attenuation
was still higher due to the wet
residue on the antenna.
It was concluded that if
the received signal was
monitored for longer,
the fading pattern could
be quantified.
2019
[132]
To research the
impact of heavy rain
on link performance
to accurately
estimate the
attenuation using
dynamic rain fade
measures to sustain
link connectivity.
The study utilized
17-year rainfall data
collected using two
different types of
measurement tools, JW
RD-80 disdrometer and
rain gauge with
three different
sampling times.
The work aims to determine
which dynamic fade mitigation to
employ; the backpropagation
neural network (BPNN) model
was utilized to anticipate the
condition of the link. The model
was validated using rainfall
events of variable magnitudes
from several rainfall regimes.
The backpropagation
neural network (BPNN)
model predicted rain
attenuation and
outperformed other
models in the
decision-making process
between rain fade
mitigation approaches.
2018
4.4. Physical Models
The physical models and a review of previous works on rain attenuation that utilized
these models for 5G networks are discussed in this section. The physical models were
developed based on the correspondence between the formation of the rain attenuation
model formulation and the physical structure of rain events. There are three physical
models, which include:
4.4.1. Crane Two-Component (T-C) Model
The model was proposed primarily for Western Europe and the United States; however,
it has difficulty estimating rainfall features such as the frequency of occurrence and mean
rainfall for weak and powerful rain cells. This rain attenuation prediction model presents
separate procedures for heavy and mild rain statistics to account for the contributions
of areas with heavy rainstorms (also known as volume cells) and larger areas of lesser
rain intensity enclosing the showers (also known as debris), as for a stratiform rain event
associated with Europe and America [133]. For a particular propagation path, the model
adopts the existence of either a sole volume cell, debris, or both. It is targeted at calculating
the probability that a certain attenuation level is surpassed, whose value might be produced
by either component of the rain process (volume cell or debris). These probabilities are
calculated independently and summed up to produce the desired estimate. At its simplest,
the model involves the following steps: (a) propagation path determination for the global
climate; (b) establishment of a mathematical link between the anticipated path length in
volume cell and debris regions; (c) determinination of the expected amount of attenuation;
(d) calculation of the required rain rate to produce rain attenuation; and (e) calculation of
the probability that the given attenuation is set in step (c) above, given by the expression in
Equation (48):
!
′′
ln Rr − ln Rd
LD
Lc
R D /Rc
e
+ PD 1 + ′′ η
(48)
P(γ) = Pc 1 +
Wc
σD
WD
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where P(γ) is the desired probability that the specific attenuation is exceeded, Pc is the
probability of a cell, PD denotes the probability of debris, η is the normal distribution
function, σD is the standard deviation of the natural logarithm of the rain rate, Wc and WD
are the length scale (in kilometers) for the cell and debris, respectively, Lc and L D are the
cell and debris respective path lengths, and R D and Rc are the rain rates for debris and
cell, respectively.
The model has been reported to work for both satellite and terrestrial links. However,
it has exhibited relative difficulty in determining some parameters, such as the probabilities
of occurrence and average rainfall for both volume cells and debris.
4.4.2. Ghiani Model
This model [41] is based on MultiEXCELL-derived rain attenuation statistics and is a
correction-based path reduction factor model for terrestrial networks. It can be expressed
mathematically as shown in Equation (49):
A=
Z
L
γRr (l )dl =
Z
aRr (l )b dl
(49)
L
Calculating the rain attenuation A assuming the rain rate Rr is constant throughout
the transmission link in terms of the path reduction factor r given by Equation (50):
A = aRr b L p r
(50)
Deriving the path reduction factor r for the rain maps generated by the MultiEXCELL model:
r = A/aRr b L p
(51)
It was also noted that the average path reduction factor (PF) trends followed an
exponential function expressed in Equation (52):
r av = x f , L p e−y( f ,L p ) R + z f , L p
(52)
where the symbols x, y, and z are regression coefficients of the path length and frequency.
By neglecting the effect of frequency, the rain attenuation A can be expressed in Equation (53):
h
i
A = aRr b L p x L p e−y( L p ) R + z L p
(53)
where the coefficients x, y, and z can be expressed as Equations (54)–(56), respectively:
x = −0.8743e−0.1111Rr + 0.9061
(54)
y = −0.0931e−0.0183Rr + 0.1002
(55)
z = −0.6613e−0.178Rr + 0.3965
(56)
4.4.3. Capsoni Model
This model [134] is made up of multiple rain cell formations known as kernels, in
which the rainfall intensity varies with distance from the center in terms of the peak
intensity as expressed in Equation (57):
R f = R pk e−ρd /ρdo
(57)
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where R f is the rainfall, ρd is the distance from the center, ρdo is the conditional average
radius, and R pk is the peak intensity; the cumulative probability of attenuation P( A) can
be expressed mathematically in Equation (58):
P( A) =
Z ∞
Re
x ·[0.5In2 ( R pk /Re ) +
i
1
In R pk /Re )]·[ − P R pk ′′′ d InR pk
4πρo
where Re is the effective rain rate and x = e−ρd /ρdo .
The rain distribution can be computed using the Equation (59):
!
∗
R
f
P R f = Po Inn
Rf
(58)
(59)
This model does not provide attenuation. However, it can be easily estimated
through a synthetic rain rate employing an appropriate estimation model. The COST
205, 1985 database was used to validate the model. EXCELL has been widely used to investigate the performance of telecommunications links. However, two drawbacks associated
with it are, on the one hand, the choice of exponential distribution of rain rate, which is not
observed in nature, and on the other hand, the overestimation of Re . The enhanced EXCELL
is said to work for both stratiform and convective rain. Table 8 presents a summary of work
that has utilized these physical models including the methodology adopted, the methods
of validation, and the results obtained.
Table 8. Summary of Rain Attenuation Using Physical Models.
Ref.
Objective of
Research
Methodology Adopted
[135]
To study the effect of
the propagation
properties of THz
waves in falling
snow and
a snow layer.
Theoretical investigation
between 100–400 GHz
band between 0–20 ◦ C.
[41]
To develop a model
for predicting rain
attenuation affecting
terrestrial links
based on
a physical approach.
The research considered
and utilized
experimental data
collected worldwide.
To provide 1-min
rainfall rates for use
in estimating the
effect of rain on the
propagation of radio
waves through the
earth-space
in Malaysia.
The study used rainfall
data from two TRMM
satellite datasets and
estimated thunderstorm
ratio over 57 locations
in Malaysia.
[136]
Method of Validation
Result Obtained
Year
Mie scattering theory is employed
to fit the measured data.
Results showed THz wave
suffers higher signal loss
in snow than in the rain
under an identical
fall rate.
2019
The relative error margin ε p was
used to validate the models and
can be expressed mathematically
as:
R ( P)− R
( P)
× 100%
ε p = pred R ( Pmeas
)
Results obtained after
analysis showed that the
model able to predict the
MultiEXCELL-derived rain
attenuation statistics with
very satisfactory accuracy
but requires
more validation.
2017
The percentage error metric was
used to validate the results with
several ground data sources from
NOAA, GPCC, and NASA.
For Malaysia,
the correlation coefficient
was 0.79–0.89, and the
average bias error
between TRMM and
GPCC was ±50 mm.
2013
meas
4.5. Optimization-Based Models
The optimization-based models emphasize the use of the optimization process in the
formulation of input parameters for additional factors affecting rain attenuation, such as
the minimum error value. This section presents and discusses three different models in the
optimization-based models category as well as provides a review of previous work done
using these models.
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4.5.1. Pinto Model
This model is an improved variant of the ITU-R P.530-17 rain attenuation prediction
model, which is likewise based on the distance correction factor r as used in the ITU-R
model, as well as the effective rainfall rate distribution (Re ) [137]. This model can be
represented mathematically as shown in Equation (60):
h
ib
A p = a x1 R p ( x2 + x3 /L p ) L p ·
x 4 d x5 R p x6 f x7
1
+ x8 x − e x9 L p
(60)
where A p is the rain attenuation at %p of time, R p denotes the rain rate at %p of time, L p
denotes the path length, and a and b are functions of frequency.
The model employs the quasi-Newton approach and particle swarm optimization
(PSO) to reduce the RMSE. The quasi-Newton multiple nonlinear regression (QNMRN) and
Gaussian RMSE (GRMSE) algorithms are used to generate the coefficients x1 , x2 , . . . , x9
which are then fine-tuned using the PSO method.
4.5.2. Livieratos Model
This regression method relies on Supervised Machine Learning (SML) that leverages
Gaussian process (GP) compatible kernel functions derived using the ITU Study Group
Databank [42]. Cross-validation was employed to evaluate the performance of the model
based on four kernel functions; however, the rain attenuation algorithm must be trained
in a specific area of interest to predict rain attenuation in a certain geography, weather, or
carrier frequency.
4.5.3. Develi Model
This model [38] was tested utilizing the Differential Evolution Approach (DEA) optimization technique at 97 GHz on a terrestrial link in the United Kingdom (UK). The model
was used to show the nonlinear relationship between the inputs (rainfall rate and percentage of time) and outputs (rainfall rate and percentage of time) (rain attenuation) given in
Equation (61):
A(t) =
H
N
h =0
n =1
∑ ah xh (t) + ∑ bn Rrn (t)
(61)
where A denotes the rain attenuation, Rr denotes the rainfall rate, x (t) denotes the time
percentage, and the sum of the parameters H and N determines the number of the input
terms in the model while the parameters a0 . . . ak and b0 . . . bn are the model parameters.
Equation (61) can be rewritten in closed form as shown in Equation (62):
A(t) = f ( x (t), y(t), a0 , a1 , . . . , aK , b1 , b2 , . . . , b N )
(62)
where the function f (·) denotes the nonlinear relationship between A(t), Rr (t), and x (t).
The mean absolute model error (E) can be defined as Equation (63):
E=
1
M
M
∑ |mk (t) − Ak (t)|
(63)
k =1
where M denotes the amount of information in the measurement set. Substituting Equation (62)
into Equation (63) gives Equation (64):
E=
1
M
M
∑ | m k ( t ) − f ( x ( t ), y ( t ), a0 , a1 , . . . , a K ,
k =1
b1 , b2 , . . . , b N )|
(64)
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The cost function for this equation is the mean absolute error, which is employed to
derive the optimized error using the DEA algorithm. The mutation operation is crucial to
the DE algorithm. The mutant vector can be written as shown in Equation (65):
ς M,i = ςn,opt + Pm (ςn,p1 − ςn,p2 ), i 6= p1 and i 6= p2
(65)
where n denotes the generation index, Pm denotes the mutation variable, p1 , p2 and i
are three arbitrarily chosen individual indexes, and the M and opt refer to the gene pool
and the optimal entity in the population, respectively. Different works that have utilized
these optimization-based models have been reviewed and presented in Table 9 including the methodology adopted, the various method of validation used, as well as the
results obtained.
Table 9. Summary of Rain Attenuation Using Optimization-Based Models.
Objective of Research
Methodology Adopted
Method of Validation
[42]
To develop a novel rain
attenuation prediction
model using Supervised
Machine Learning (SML)
and the Gaussian
process (GP)
for regression.
The study used
experimental data
retrieved from the ITU-R
databank, which
includes 89 experimental
links located in various
countries with
operational frequencies
ranging from 7 to
137 GHz at 0.5 to 58 km.
A 5-fold Cross-validation
approach was employed to
evaluate the model. However,
the RMS was calculated to
compare the model to
otherqmodels:
ρv = µ2v + σv2
[138]
To estimate rain rate
using measured rain
attenuation for Tokyo
Tech mmwave
model network.
The study utilized a
fixed wireless access link
with an antenna having
a high gain of 29 dBi,
where rain rate data was
recorded every
5 seconds.
The rain attenuation is
calculated using the estimated
clear-weather level.
Result Obtained
Year
The model outperformed
the four prediction models
under consideration,
including the ITU-R,
Silva-Melo, Moupfouma,
and Lin models.
2019
From the measurement
and estimation, it was
shown that the error
between them was
between 0.1–0.3 dB.
2010
Table 10 presents and classifies the various rain attenuation prediction models in terms
of their input parameters or functions such as path length, frequency, rain rate, etc.
Table 10. Input Parameters of the Existing Terrestrial Rain Attenuation Models.
Effective
Path Length
Effective
Rainfall Rate
Time Series
✓
✓
✓
✓
✓
✓
✓
✓
✓
×
×
×
×
✓
✓
✓
✓
✓
[108]
✓
✓
✓
✓
[43]
✓
✓
✓
✓
Polarization
✓
Rain Rate
✓
Frequency
×
✓
×
×
Path Length
×
References
×
Models
Empirical
Models
Rain Rate
Exceeded
Parameters
Model
Category
Ref.
Garcia
[106]
✓
✓
✓
✓
Crane
[37]
✓
✓
✓
✓
Mello
[40]
✓
✓
✓
Moupfouma
[39]
✓
✓
Perić
[139]
✓
Abdulrahman
[107]
Da Silva
Budalal
×
×
×
×
✓
×
×
✓
✓
✓
✓
×
×
✓
×
×
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Table 10. Cont.
Model
Category
Models
References
Path Length
Frequency
Rain Rate
Polarization
Rain Rate
Exceeded
Effective
Path Length
Effective
Rainfall Rate
Time Series
Parameters
Statistical
Models
ITU-R
[140]
✓
✓
✓
✓
✓
✓
[63]
✓
✓
✓
×
×
×
Singh
×
×
Fade-Slope
Models
Andrade
[128]
✓
✓
✓
✓
Physical
Models
Optimization-Based Models
×
×
×
×
×
×
×
×
×
×
×
×
Chebil
[129]
✓
✓
✓
✓
Crane T-C
[133]
✓
✓
✓
✓
Ghiani
[41]
✓
✓
✓
✓
Capsino
[134]
✓
✓
✓
✓
×
Pinto
[137]
✓
✓
✓
✓
✓
Livieratos
[42]
✓
✓
✓
✓
✓
Develi
[38]
×
×
✓
×
×
×
×
✓
×
×
×
×
×
×
✓
×
×
✓
✓
×
×
×
✓
This section has reviewed the various existing models used in modeling rain attenuation. Eventually, it grouped them into five categories: empirical, statistical, physical,
fade-slope, and optimization-based models, which can be employed to estimate attenuation
due to rain in tropical locations. According to the reviews, it can be concluded that none of
the prediction models can be considered a complete model sufficient to accurately meet all
demands for various infrastructure setup characteristics, geographic regions, or climate
variations. From the taxonomy table, it can be seen that most of the models took into
consideration the path length, frequency, and polarization, except the Develi model, which
only considered the rain rate and time-series parameters.
5. Total Attenuation
This section examines signal attenuation due to the cloud, rainfall, atmospheric gases,
and the total propagation attenuation loss, as well as providing a summary of these models
and their input parameters.
5.1. Propagation through Cloud
Cloud liquid water content is another atmospheric element, apart from rain, that
absorbs and scatters electromagnetic signals, especially for frequencies above 10 GHz,
propagating from the sender to the receiver, causing attenuation of the signal. The impact
of a cloud on signals is less than that of rain since the attenuation is determined by the
cloud’s properties, such as its width, depth, and thermal readings (temperature), unlike rain
which takes into consideration the communicating system’s parameters [141]. According
to [142], cloud attenuation can be measured or quantified using the liquid water content
and can be mathematically expressed as shown in Equation (66):
Acl = γc
Lwc
sinθ
(66)
where Lwc is the liquid water content, θ is the angle of elevation, and γc cloud-specific
attenuation coefficient, which can be expressed mathematically as shown in Equation (67):
γc =
0.819 f
ε′′
h
1−
2+ ε ′ 2
ε′′
i
(67)
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where ε is the complex dielectric permittivity of water contents within the cloud.
ε′ =
ε0 − ε1
ε1 − ε2
2 +
2 + ε 2
f
f
1 + fr
f rsec
(68)
pri
ε′′ =
f (ε − ε )
f (ε − ε )
1 2
0 1 +
2
2
f
f
f r pri 1 + f r
f rsec 1 + f rsec
(69)
pri
f r pri
300
ε 0 = 77.6 + 103.3
−1
Tk
2
300
300
− 1 + 294
−1
= 20.09 − 142
T
T
300
f rsec = 590 − 1500
−1
T
(70)
(71)
(72)
where f r pri and f rsec denote the principal and secondary relaxation frequencies, respectively, T is the temperature, and the values of ε 1 and ε 2 are 5.48 and 3.51, respectively.
5.2. Propagation through Rain
Rain, one of the main dynamic natural occurrences, reduces the power of transmitted
electromagnetic signals due to absorption and dispersion depending on the rainfall rate
and the physical structure, such as the width, height, and number of droplets that the
signal passes through [34,142]. The universal power-law model used to describe the rain
attenuation and specific attenuation are provided in Equations (6) and (7). The relationship between the path length (L p ) and the path reduction factor (r) is also provided in
Equation (29). The power-law parameters a and b can be derived using the following
Equations (73) and (74):
2
4
log a =
−(
log f c −y j
zj
−(
log f c −yi 2
)
zi
∑ xj e
)
j −1
5
log b =
∑
xi e
j =1
+ mk log f + zk
(73)
+ mn log f + zn
(74)
!
The rain rate Rr can be derived in terms of the total depth of water droplets caused by
rain (mm) and the total time of rainfall (hrs.) as expressed in Equation (75):
Rr =
Total depth of rainfall
Entire rainfall duration
(75)
5.3. Propagation through Atmospheric Gases
Numerous gases, including oxygen and water vapor, are present in the atmosphere.
These gases have varying heights, loftiness, and breadths, resulting in varying degrees of
multipath attenuation of electromagnetic signals [34]. The attenuation caused by oxygen
can be distinguished from all other atmospheric impairments. Its impact is consistent
across all regions and it is not dependent on any meteorological parameters, unlike the
attenuation due to water vapor which absorbs and scatters the signal and is based on
meteorological properties such as temperature, water vapor content, and height above sea
level [143]. According to [144], the attenuation caused by water vapor (for f ≤ 350 GHz)
and oxygen (dry air) can be calculated, respectively, as shown in Equations (76) and (77):
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Awv
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−2
−3
3.27 × 10 rt + 1.67 × 10
=
3.79
( f −183.31)
Aox =
"
2
ρrt7
rp
+ 7.7 × 10−4 f 0.5 +
+11.85r2p rt
+
3.79
( f −22.235)2 +9.8r2p rt
4.0lrt
( f −325.153)2 +10.44r2p rt
+ . . .
2
−4
f ρr p rt × 10
#
7.27rt
7.5
+
f 2 r2p rtt × 10−3 for f ≤ 57 GHz
f 2 + 0.351r2p rt2
( f − 57)2 + 2.44r2p rt5
(76)
(77)
where Awv denotes the attenuation caused by water vapor, f denotes the frequency (GHz),
r p = p/1013, rt = 288/(273 + T ), p is pressure, T is temperature, and Aox is the attenuation
caused by oxygen. The total attenuation due to atmospheric gases for both uplink and
downlink transmission can be expressed as shown in Equation (78):
hox Aox + hwv Awv
A gs =
(78)
sinθ
where hox and hwv are the equivalent height for oxygen (dry air) and water vapor, respectively, and θ denotes the angle of elevation.
5.4. Propagation through Radome
A radome, coined from the two words radar and dome, is a weatherproof enclosure
constructed of structural plastic to protect the surface of an antenna, such as a microwave
or radar antenna, from external environmental disturbances like wind, rain, ice, sand,
and ultraviolet rays, and also to conceal the electronic equipment of the antenna from
the public [145,146]. The radome can attenuate the receiving and transmitting signals,
especially when wet; hence, it should be constructed using low-permittivity materials,
shaped to achieve good transparency for the desired frequency, and hydrophobic-coated
to avoid additional attenuation due to the wet radome surface [147,148]. Attenuation due
to radome occurs by reflection and absorption based on the signal frequency as well as
the thermal reading (temperature) and width of the water slab [149]. A simple model was
utilized by [150] to calculate the overall radome attenuation through a two-layer structure
and expressed as in Equation (79)
Arad
"
T1 T2 T3 e− j(τr +τw )
= −10 log
1 + Γ1 Γ2 e− j2τr + Γ2 Γ3 e− j2τw + Γ1 Γ3 e− j2(τr +τw )
#2
(79)
where τr and τw denote the electrical thickness of the radome and water layers, respectively,
expressed as given in Equations (80) and (81):
p
τr = k o ε r dr
p
τw = k o ε w dw
(80)
(81)
where k o denotes the free-space wavenumber, ε r denotes the complex relative dielectric
constants of the radome material, ε w denotes the complex relative dielectric constants of
the water at the X band, while dr and dw denote the physical thickness of the radome and
water layer, respectively.
T1,2,3 and Γ1,2,3 denote the respective transmission and reflection coefficients for the
electric field at the (1) air–radome, (2) radome–water, and (3) water–air interfaces, and expressed as shown in Equations (82)–(85):
Γ1 =
√
1 − εr
√
1 + εr
(82)
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√
√
εr − εw
√
Γ2 = √
εr + εw
√
εw − 1
Γ3 = √
εw + 1
(83)
(84)
T1,2,3 = 1 + Γ1,2,3
(85)
The thickness of water, according to [149], can be related to the rainfall rate using
Gibble’s Equation (86):
3µk ð Rr 1/3
tw =
(86)
2g
where tw is the thickness of the water layer, µk denotes the kinematic viscosity of water
(kg/m/s), ð denotes the radius of the radome, Rr denotes the rainfall rate, and g denotes
the gravitational acceleration.
5.5. Total Propagation Attenuation Loss
The total attenuation is a critical parameter to consider as it provides the necessary
information for effectively designing communication links such as Earth–satellite and
terrestrial communication links. The total attenuation, as defined by [45], is the sum
of the individual attenuation parameters, including attenuation caused when the signal
propagates through free space, clouds, rain, atmospheric gases, radome, etc. Hence, the total
attenuation can be mathematically expressed as shown in Equation (87):
Tatt = AnL + A f s + Acl + A + A gs + Arad
(87)
where AnL is the attenuation loss due to non-line of sight, A f s denotes the free space
attenuation as expressed in [151], Acl denotes the attenuation due to cloud Equation (66),
A attenuation due to rain Equation (6), A gs attenuation due to atmospheric gases (dry air
and water vapor) Equation (78), and Arad attenuation due to radome Equation (79).
Table 11 presents the various input parameters of the different atmospheric impairments models as well as radome for total attenuation.
Table 11. Input Parameters of the Atmospheric Impairments Models for Total Attenuation.
Temperature
Dielectric
Constant
Pressure
Thickness
Polarization
Equivalent
Height
✓
×
×
×
×
×
✓
Rain
[34]
✓
✓
Cloud
[142]
✓
×
Atmospheric Gases
[144]
✓
Radomes
[150]
×
×
×
×
×
✓
✓
✓
×
×
✓
×
×
✓
×
×
✓
×
✓
✓
×
×
×
×
Gain
Distance
✓
Wavelength
Frequency
[152]
Rain Rate
References
Free Space
Angle of
Elevation
Atmospheric
Impairments
Parameter
×
×
✓
✓
✓
✓
✓
×
×
×
×
✓
✓
✓
✓
×
×
×
✓
×
×
×
×
5.6. Review of Total Attenuation Models
This section reviews various signal propagation models used to calculate attenuation
as signals travel through various media, including free space, clouds, rain, radomes,
and atmospheric gases (water vapor and dry air). These are summarized in Tables 12–14
for propagation through the cloud, atmospheric gases, and radome, respectively.
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Table 12. Summary of Attenuation through the Cloud.
Ref.
[153]
[154]
[155]
[156]
[157]
Freq.
20–200 GHz
32 GHz
30–300 GHz
60 GHz and
above
18.9 GHz
Location
Method of Validation
General Comments/Findings
Year
14 locations
in Europe
(not specified)
The average error coupled with
the RMS was used to validate the
model’s accuracy.
The results show a very good
prediction performance with an
overall RMS of the approximation
error of 3.4%, slightly dependent on
the frequency between 60 and
170 GHz.
2014
Cebreros, Spain
The cloud-specific attenuation
contribution was modeled with a
stochastic process well defined in
both amplitude and
time domains.
The time variations of the simulated
stochastic process can simulate the
behavior of a real cloud attenuation
contribution in a good manner, even
in the time domain.
2019
Northern
(Sodankyla,
Finland) and
Southern
(Trapani, Italy)
Europe
The average error coupled with
the RMS was used to validate the
model’s (SMOC) accuracy.
High-resolution three-dimensional
cloud fields were developed using
this model. Results showed that,
when taking into account all sites,
the average RMS of the error on the
CCDF of the cloud liquid water
content is equivalent to 0.09 mm,
which demonstrated good agreement
with the other estimation made using
other data.
2014
Durban and Cape
Town in South Africa
This study utilized two separate
sites and evaluated the coefficient
of the specific attenuation against
the water droplets at various
temperatures as a function of
the frequency.
Results based on several notable
cloud characteristics revealed that
specific attenuation coefficients and
cloud attenuation increase with
frequency, demonstrating the
influence of the LWC on signals.
2021
The various models were
validated using the yearly
(2014 and 2015) cloud-induced
attenuation CCDF.
The ITU-R model underestimates the
cloud attenuation in tropical regions,
according to the results of the yearly
CCDF, which indicated that at
0.01%-time percentage,
the attenuation due to cloud could
range up to 4.2 dB in tropical regions.
2017
Nanyang
Technological
University (NTU),
Singapore
Table 13. Summary of Attenuation through Atmospheric Gases.
Ref.
[158]
[159]
Freq.
20–100 GHz
1–350 GHz
Location
Method of Validation
General Comments/Findings
Year
24 sites worldwide
The average error coupled with
the RMS was used to validate the
prediction accuracy following
ITU-R standard P.311-15.
Results indicated that the model’s
prediction accuracy improves
significantly for the current
recommendation and is less
dependent on the operational
frequency (20–100 GHz range) and
the considered site.
2016
24 sites worldwide
The average and root mean
square of the error figure were
used to validate the prediction
accuracy following ITU-R
standard P.311-15.
The proposed method outperforms
the other methods listed in ITU-R
Annex 2 (P.676-10) Rec., according to
the results of an evaluation against a
large sample of radiosonde data.
2017
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Table 13. Cont.
Ref.
[160]
[79]
[123]
Freq.
10–350 GHz
1–350 GHz
4–40 GHz
Location
Method of Validation
General Comments/Findings
Year
24 sites worldwide
The proposed model was
evaluated against the RAOBS
data sample for predicting the
path oxygen attenuation in terms
of the average estimation error as
well as the RMS.
The obtained results demonstrated a
very excellent level of accuracy in
terms of overall prediction error and
performance stability, which turns
out to be slightly
frequency-dependent and almost
site-independent.
2017
Spino d’Adda, Italy
The mean and root mean square
values of the prediction error
calculated every 5 s were used to
validate the model’s accuracy.
Results indicated that version 11 of
ITU-R P.676 significantly
underestimates the attenuation due
to gases, while the previous version is
accurate enough to be used to
estimate the tropospheric attenuation.
2019
The gaseous attenuation for West
Africa was estimated and
validated using the ITU-R P
676 model.
Results showed that C and Ku bands
have low signal fade, whereas the Ka
and V bands have higher signal fade
for both oxygen and water vapor.
Additionally, the western section of
West Africa showed a larger increase
in attenuation due to gas than the
southern part of West Africa.
2018
Nigeria, West Africa
Table 14. Summary of Attenuation through Radome.
Ref.
[161]
[149]
[146]
[147]
Freq.
Methodology Adopted
Method of Validation
General Comments/Findings
Year
150–300 GHz
The research used a
Stepped Frequency
Radar (SFR) and a
Frequency Modulated
Continuous Wave
(FMCW) Radar to
collect measurements.
The measured results were
compared to the Fresnel theory of
transmission and reflection for
multilayer structures in the study.
The obtained results were in good
agreement with the theoretical
model that explains the signal loss
caused by layers of water on a
radome. The results also revealed a
significant correlation between
consistent water layer thickness
and signal reduction.
2016
8–12 GHz
(X-band)
An antenna was
employed in the study
as a time-domain
reflectometer
and probe.
A laboratory that measures the
reflectance produced by radome
panels at the X band was
designed to evaluate the
designed system.
The results revealed that when
absorption is negligible, the novel
instrument for characterizing the
influence of a radome in dry and
wet conditions can be used to
provide reliable results.
2018
1–14
GHz
The proposed
multi-layer radome
design methodology is
based on multiple
structures, a-sandwich,
which was not employed
in typical radomes
with multilayers.
A radome with multilayers and
ultra-wideband features
operating between 1 to 14 GHz
was proposed, constructed,
and assessed to validate the
proposed design methodology.
The results demonstrated a strong
correspondence between the
calculated and measured results
with less than 0.1 dB absolute error
for all scanning angles.
2020
The study used ARPA
Piemonte polarimetric
X-band radar (ARX)
data and two validation
procedures.
The first method estimated
two-way wet radome losses using
an empirical model based on
self-consistency, whereas the
other method evaluated the radar
accumulations against the rainfall
gauge measurements with and
without radome adjustment.
Results obtained based on the
rainfall comparisons showed that
the self-consistency method is an
efficient real-time correction of the
effects introduced by a wet radome.
2013
8–12 GHz
(X-band)
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6. Specific Attenuation
This section presents and discusses the specific attenuation models calculation based
on rainfall, atmospheric gases (oxygen and water vapor), and clouds.
6.1. Specific Attenuation Model Due to Rainfall
As described earlier, Equations (6), (7), (29), and (73)–(75) provide detailed relationships of specific attenuation (dB/km) due to rainfall across terrestrial communication
channels and also the relationship between the specific attenuation and rainfall rate,
frequency, and polarization characteristics.
6.2. Specific Attenuation Model Due to Atmospheric Gases
The specific attenuation due to atmospheric gases, according to [144], can be precisely
calculated as the sum of the individual spectral lines from oxygen and water vapor at any
value of pressure, temperature, and humidity along with a few additional parameters as
shown in Equation (88):
′′
′′
(88)
γ = γo + γw = 0.1820 f Nox ( f ) + Nwv ( f )
where γo and γw denote the specific attenuation (dB/km) for oxygen and water vapor,
′′
′′
respectively, f denotes the frequency (GHz) while Nox ( f ) and Nwv ( f ) are the imaginary
parts of the frequency-dependent complex refractivity expressed as in Equations (89) and (90):
′′
Nox ( f ) =
∑
i (ox )
′′
Nwv ( f ) =
′′
Si Fi + ND ( f )
∑
Si Fi
(89)
(90)
i (wv)
where Si denotes the strength of the ith oxygen or water vapor line, Fi denotes the oxygen
′′
or water vapor line shape factor, and ND ( f ) denotes the dry continuum due to pressureinduced nitrogen absorption and the Debye spectrum as given by Equation (91). That is,
6.14 × 10−5
1.4 × 10−12 pϕ1.5
′′
ND ( f ) = f pϕ2
h i2 + 1 + 1.9 × 10−5 f 1.5
f
∂ 1+ ∂
(91)
where ∂ denotes the width parameter for the Debye spectrum expressed in Equation (92):
∂ = 5.6 × 10−4 ( p + e) ϕ0.8
(92)
The line strength Si can be obtained for both dry air and water vapor using the
following Equations (93) and (94):
Si = a1 × 10−7 pϕ3 e a2 (1− ϕ) For dry air (oxygen)
(93)
Si = b1 × 10−1 eϕ3.5 eb2 (1− ϕ) For water vapor
(94)
where T is the temperature in Kelvin, ϕ = 300/T, p denotes the oxygen pressure (hPa),
and e is the water vapor partial pressure (hPa). Hence, the total barometric pressure can be
expressed in Equation (95):
ptot = p + e
(95)
6.3. Specific Attenuation Due to Clouds
It has been shown in [162] that the Rayleigh Scattering Approximation is accurate for
frequencies up to 200 GHz for clouds or fog that contain predominantly small droplets of
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diameter less than 0.01 cm and the specific attenuation due to the cloud can be expressed
in Equation (96):
γc ( f , T ) = K l ( f , T ) M
(96)
where γc denotes the specific attenuation within the cloud (dB/km), f denotes the frequency, T denotes the cloud liquid water temperature (Kelvin), M denotes the liquid
water density in the cloud or fog (g/m3 ), and Kl denotes the cloud liquid water-specific
attenuation coefficient, which can be represented mathematically as Equation (97):
Kl =
γc
M
(97)
Table 15 presents the various input parameters of the different atmospheric impairments models for specific attenuation.
Table 15. Input Parameters of the Atmospheric Impairment for Specific Attenuation.
[144]
✓
✓
✓
✓
Cloud or Fog
[162]
✓
×
✓
×
Density
Dry Air and Water Vapor
Effective
Path Length
×
Dielectric
Permittivity
Temperature
✓
Rain Rate
Distance
✓
Drop Size
Distribution
Frequency
[44]
Polarization
References
Rain
Pressure
Atmospheric
Impairment
Parameters
×
✓
✓
✓
✓
×
×
×
×
×
×
×
×
×
✓
×
×
×
✓
6.4. Review of Total Attenuation Models
This section reviews various signal propagation models used to calculate specific
attenuation as signals travel through various media, such as clouds, rain, and atmospheric
gases (water vapor and dry air). Table 16 presents the summary of specific models.
Table 16. Summary of Specific Attenuation Models.
Ref.
Objective of Research
Methodology Adopted
Frequency
Method of Validation
Year
[112]
To establish a repository of k
and α values for the frequencies
up to 1000 GHz.
Logarithmic regression was applied
to Mie’s scattering calculations,
Laws and Parsons,
and Marshall–Palmer DSD on
widespread and convective rain.
1–1000 GHz
Comparison with direct
measurement values.
1978
[163]
To establish a relationship
between the regression
coefficients of attenuation
(a and b) and frequency.
The study employed the power law
to estimate the relationship between
the regression coefficients and the
frequency analytically with a rain
rate range of 5–100 mm/h.
1–400 GHz
The work validates the model; the
ITU-R database was utilized.
However, to compare the model
to other models, their absolute
and relative errors were
calculated and compared.
2001
[54]
To review works done in
attenuation and investigate
prediction models.
The study utilized the reduction
factor and frequency scaling to
predict total attenuation.
15 GHz, 23 GHz,
26 GHz and
38 GHz
The ITU-R database is used
to validate.
2013
[164]
To propose a novel
methodology using standard
equipment for the calibration in
real-time of the
power-law parameters.
Real measurements logged by
CMNs and a standard rain gauge
were employed to calibrate the
parameters of the power law.
10–100 GHz
Calibrated power-law parametric
values were validated using the
ITU-R values.
2016
[42]
To develop an enhanced rain
attenuation prediction model
with a universal perspective.
The study employed supervised
machine learning (SML) to
formulate enhanced models.
30–300 GHz
The R2 measure is used to express
the efficacy of the regression.
2019
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The power law has been used to calculate rain attenuation since the 1940s and is still
being used to estimate the attenuation by network designers and operators. The ITU-R
standard has provided a simplified technical standard that guides the establishment of
the power-law based correlation between the attenuation due to rain and the rainfall rate
(P.838-3). More recently, the power law was explored for monitoring rainfall occasioned by
the availability of attenuation data collected from the Commercial Microwave Networks
(CMNs) backhaul infrastructure. This advancement has paved the way for opportunistic
surveillance instruments that require little or no additional hardware or cost. Also, more
recently, supervised machine learning (SML) is gaining traction in the quest for calibrating
the power-law parameters.
7. Review of Different Methods of Model Validation
This section presents a review of different model validation methods and summarizes
model validation techniques.
In the literature, when new mathematical models are developed, there is usually a
means to validate the model. For rain attenuation, the models are subjected to different
validation tests to determine their ability to predict rain attenuation. According to the
ITU-R, there are standard procedures for testing the validity of mathematical models
developed for rain attenuation predictions. As a result, it is necessary to analyze some
of these models to determine the current and future developments in this area. Some of
the works of literature in this field were noted in [165]. In this context, this study has
executively selected four model validation methodologies based on the recommendation
of the ITU-R [166] which are available in the literature. The methodologies are (I) an
input-to-output correlation or coefficient of determination, (II) Root Mean Square Error
(RMSE) and RMS functions, (III) goodness-of-fit function, and (IV) Chi-square models.
The coefficient of determination function is defined as the total variations in a proposed
model or, in some cases, multiple regression models. Mathematically, it is defined in
Equation (98):
Explained Variation
R2 =
(98)
Total Variation
Root Mean Square Error (RMSE) is utilized to measure the difference in numerical
estimation and can be expressed mathematically as given in Equation (99):
RMSE =
s
∑iN=1 (Oi − Pi )
N
2
(99)
Another variant of the RMSE function is the Spread-Corrected RMSE (SC-RMSE) as
expressed in Equation (100):
SC_RMSE =
where:
s
1
n
n
∑
ε p′
2
(100)
i =1
ε p ′ = ε p − σp
(101)
The goodness-of-fit function ε( p) T can be used to test how well the developed model
observed data fits the predicted data and this can be expressed in Equation (102):
ε( P)T =
A p,predicted − A p,measured
× 100 [%]
A p,measured
(102)
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In some cases, this is called the Pearson goodness-of-fit function, and the expression
for this is defined in Equation (103):
2
X =
∑
j
O j − Ej
Ej
2
(103)
where O j is the observed count in cell j and Ej is the expected count in the cell j when
0.001% < p < 1%.
Chi-square can also be used to validate developed models and can be expressed
mathematically as defined in Equation (104). The Chi-square statistics were employed to
evaluate the method’s performance.
X2 =
( A p,predicted − A p,measured )2
A p,predicted,i
i =1
N
∑
(104)
The difference between the predicted rain rate value and the measured rain rate value
is given by relative error (ε p ) expressed in Equation (105):
ε p = R p − Rm
(105)
where R p : is the predicted value and Rm is the measured rain rate estimated for
0.001% < p < 1%. The maximum error and the mean error can be expressed mathematically as shown in Equations (106) and (107), respectively:
Maximum error = max ε p
(106)
Mean error E =
1
n
n
∑ εp
(107)
i
Rank Correlation, ρ
This measures the strength of the relationship of related data. It does not assume
measurement for statistical dependence between the measured and predicted; hence, it is
non-parametric. Mathematically, it can be expressed as shown in Equation (108):
∑ p (R p − R p ) Rm − Rm
ρ= q
2
3 −1 < ρ < 1
∑ p R p − R p ∑ p Rm − Rm
(108)
where R p & Rm are mean measured and predicted rain rates for 0.001% < p < 1%.
Table 17 presents the properties of the various existing rain attenuation models based
on the thresholds for each parameter considered when developing the models including
the predicted rain attenuation value range.
From Table 17, it can be seen that rain attenuation increases with increasing rainfall
rates. Furthermore, as the time percentage increases, the rain attenuation values decrease.
For example, at p = 1%, the attenuation value can be as low as 1.01 dB, whereas the attenuation can be as high as 40.48 dB at 0.001% [94]. However, according to ITU recommendations,
a low value for the standard deviation and root mean square (RMS) for the majority of time
percentages indicate that the proposed model is highly accurate [167].
Table 18 presents a summary of some works that have successfully utilized any of the
aforementioned model validation techniques to evaluate the performance of their respective
proposed models.
From Table 18, it is clear from the method of validation that root mean square (RMS)
is the method that most of the researchers are using to test the accuracy of the developed
models. Some methods that have not been given attention are the Kolmogorov–Smirnov
and the Anderson–Darling tests.
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References
Path Length (km)
Frequency (GHz)
Percentage of Time
(%)
Rain Rate (mm/h)
Rain Rate (min)
Attenuation (dB)
Number of Years
Garcia
[106]
12–58
10.8–36
0.0001–0.1
140
1
10–60
2
Crane
[37]
0–22.5
11–36.5
0.001–2
140
5
10–60
24
Mello
[40]
0.5–58
–
0.001–0.1
140
–
–
–
Moupfouma
[39]
0.1 and 3.2
38
0.001–0.1
33
1
15.65–64.54
3
Perić
[139]
2–4
80
0.001–0.1
30 & 45
–
15–30
2
Abdulrahman
[107]
0.1 and 3.2
38
0.001–0.1
0–100
1
6.03–40.66
3
Da Silva
[108]
0.5–58
7–137
0.001–0.1
140
–
–
81
Budalal
[43]
0.3
25–75
0.001–10
–
1
10–50
1
Statistical
Models
ITU-R
[140]
0.1 and 3.2
75
0.001–0.1
0–100
1
3.22–15.71
3
Singh
[63]
–
10–100
–
10–300
–
5–60
–
Fade-Slope
Models
Andrade
[128]
12.8–43
14.52–14.55
–
–
–
1–30
1–2
Chebil
[129]
0.3
15–38
–
–
2
1–15
16 months
Crane T-C
[133]
1.3–58
7–82
0.001–0.1
140
–
1–30
–
Ghiani
[41]
1–20
10–50
0.001–0.1
54
1
0–15
1–10
Capsino
[134]
–
12–18
–
4
–
–
59
Pinto
[137]
0.5–58
1–100
0.001–0.1
–
–
0–70
–
Livieratos
[42]
0.5–58
7–137
0.001–0.1
4.5–230
–
0–50
–
Develi
[38]
6.526
97
0.1–1
1.3–6.86
–
7.85–24.79
1
Model
Category
Models
Table 17. Properties of the Existing Rain Attenuation Prediction Models.
Empirical
Models
Physical
Models
OptimizationBased
Models
Table 18. Summary of Model Validation Techniques.
Ref.
Validation Technique
Comments/Findings
Year
[165]
Percentage error and RMS
Four rain attenuation models were compared in terms of the percentage error
and root mean square to evaluate the performance over six operational
point-to-point microwave links.
2014
[71]
Mean Error and Mean RMS
It has been established that the enhanced synthetic storm technique shows better
accuracy and reliability for rain attenuation prediction EHF on a statistical basis
(direct) and event basis (frequency scaling).
2020
[41]
Mean Error and Mean RMS
The proposed model was tested against the Brazilian model and the ITU-R model.
There is a need to include the dataset in the ITU-R DBSG3 database for optimal or
superior accuracy of the rain attenuation.
2017
[168]
Mean Error and Mean RMS
This work proposed a novel model based on an exponential profile of the rain cell
because the rain attenuation model consistently increases with both time
percentage, rain rate, and elevation angle. Eventually the new model outperforms the
previous models in terms of prediction and anomalous behavior.
2018
[39]
RMS
The proposed model can predict when rain attenuation would be exceeded on
both SHF and EHF radio waves.
2009
[137]
RMS
Non-linear regression is used to derive a model for rain attenuation. The result
was based on experiments in both temperate and tropical regions. Also, model
finetuning was carried out using PSO.
2019
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Table 18. Cont.
Ref.
Validation Technique
Comments/Findings
Year
[94]
The goodness of fits and
Pearson goodness of fits
Different matrices were used to evaluate the performance of the permanent
models. Furthermore, ITU-R P.530-16 and Abdulrahman models outperform at
38 GHz. However, ITU-R P.530-16 yields a better estimate at 75 GHz with a lower
error probability.
2017
Chi-square
The proposed rain, site diversity, and rain scattering predictions were developed,
and the model was tested on data collected in Europe using a satellite SIRIO and
OTS. The results were excellent, and the efficacy of the statistical method
was developed.
1987
[134]
8. Machine Learning-Based Rain Attenuation Prediction Models
This section presents the reviews of machine-learning-based rain attenuation prediction models that have been proposed to date (August 2022) and a taxonomy. Also, a brief
review of the issues with aerial communication is provided. Table 19 summarizes the
machine learning-based rain attenuation models.
Table 19. Summary of Machine Learning-Based Models.
Ref.
Objectives
Methodology Adopted
Method of Validation
Comments/Findings
Year
[169]
To predict rain
attenuation for multiple
frequencies using a
machine learning-based
estimation approach and
to compare with
other models.
The study utilized a
radiometer and laser
precipitation monitor to
obtain data for
frequencies 22.234, 22.5,
23.034, 23.834, 25, 26.234,
28, and 30 GHz.
Minimum Mean Squared
Error (MMSE) and Root Mean
Square (RMS) were used to
compare the proposed
machine, the learning-based
adaptive spline model, to the
power-law model.
Results showed that the
estimation values
obtained by the proposed
model are more accurate
than those obtained by the
power-law model.
2021
[170]
To propose a novel deep
learning architecture
that predicts future rain
fade using satellite and
radar imagery data as
well as link power
measurement.
The study chose
7 collocated locations for
Echostar 19 and 24 and
utilized data from the
4th quarter of 2018 to
the 1st quarter of 2021.
The proposed model was
compared with other machine
learning-based approaches
and evaluated in terms of
accuracy, precision, recall,
and f1-score for both longand short-term prediction.
Results showed that the
proposed model
outperforms the other
models in terms of
accuracy, recall, precision,
and f1-score, especially for
long-term prediction.
2021
[171]
To accurately predict
rain attenuation using
Backpropagation Neural
Network (BPNN)
technique.
The study utilized data
from three terrestrial
microwave links
operating at 23 and
38 GHz frequencies.
The proposed model was
validated using 38 GHz fade
slope data as well as a
chi-square fitness test.
Results showed that the
BPNN model is efficient
for the prediction of rain
attenuation in Nigeria.
2021
[172]
To compare various
models and perform
real-time prediction of
rain attenuation data for
the Earth–Space
communication link
(ESCL).
The study utilized
12-year data obtained
from the South Africa
Weather Service, where
the data was split into
two for training and
testing the proposed
network.
Comparison between the
ANN rain-induced
attenuation with existing
models such as ITU-R and
Moupfouma models were
used to validate the
performance of the model.
The result showed that the
ANN-based model
produced more accurate
results with minimum
errors than the ITU-R and
Moupfouma models.
2020
[173]
To design a new model
for calculating the
specific attenuation due
to rain at various rain
rates using machine
learning techniques
The study gathered data
from the ITU-R model
for defined values of a
and b at different
frequencies, which was
used for the training of
the model using Python.
A comparison between the
proposed model and the
ITU-R model was conducted.
Results showed that the
accuracy obtained in the
proposed model was
approximately 97%.
2020
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Table 19. Cont.
Ref.
Objectives
Methodology Adopted
Method of Validation
Comments/Findings
Year
[96]
To predict rain rate and
attenuation using a
trained backpropagation
neural network (BPNN)
in the sub-tropical
region of Durban,
South Africa.
This study utilized a
JWD RD-80 disdrometer
to collect 4-year training
and 1.5-year validation
data for a sampling time
of 30 s.
The performance of the
trained BPNN was evaluated
using the mean square error
and TANSIG transfer function
and validated using the
1.5-year data, then compared
with the ITU-R model.
Results showed a
relatively small margin of
error between predicted
rain attenuation exceeded
at 0.01% of
an average year.
2019
[174]
To show how ANN can
be employed for rain
attenuation prediction
and to compare rain
attenuation estimated by
ANN with that of the
ITU model in specific
locations in Nigeria.
The study utilized
7-year data from
6 locations to train the
ANN object, created
using a feed-forward
backpropagation neural
network learning
algorithm.
To test the prediction
performance of the trained
ANN, 3-year data were fed
into it. Then to evaluate the
ANN, a comparison with the
ITU-R model was carried out
in terms of the mean
squared error.
Results showed that the
predicted values of the
ANN almost correspond
with the calculated value
of the ITU-R model with a
mean squared error of less
than 1 dB.
2019
[175]
To investigate rain
attenuation models that
used simple ANNs with
a single hidden layer
and propose a method
for expanding
databases.
The study utilized a
stepwise methodology
comprising 6 steps for
the method of
expanding databases,
such as data selection,
data validation, etc.
The physical consistency test
was used to validate the
results obtained.
Results showed that a
simple ANN-based model
could perform better than
existing models if trained
properly using
a large database.
2019
[176]
To present an improved
rain attenuation
prediction in satellite
communication using
ANN models in four
provinces of
South Africa.
The study utilized 5-min
integration time data
obtained from the South
Africa Weather Services
based on 68.5E Intelsat
20 (IS-20) satellite
footprint and a
downlink frequency of
12.75 GHz.
A comparison was carried out
between the ANN models,
ITU-R model, and the SAM
model in terms of Root Mean
Square Error (RMSE) and
Mean Square Error (MSE).
Results showed that the
ANN models were able to
estimate rain attenuation
for all the selected
locations accurately and
outperformed both the
ITU-R and SAM models.
2019
[42]
To develop a novel rain
attenuation prediction
model using Supervised
Machine Learning (SML)
and the Gaussian
process (GP)
for regression.
The study used
experimental data
retrieved from the ITU-R
databank, which
includes 89 experimental
links located in various
countries with
operational frequencies
ranging from 7 to
137 GHz at 0.5 to 58 km.
The model outperformed
the four prediction models
under consideration,
including the ITU-R,
Silva-Melo, Moupfouma,
and Lin models.
2019
[177]
To utilize Feedforward
Backpropagation Neural
Network as a technique
for predicting rain
attenuation in satellite
links at higher frequency
in South Africa.
The study utilized 5-min
integration time rainfall
data obtained from four
provinces by the South
Africa Weather Services
(SAWS) over ten years.
Root Mean Squared Error
(RSME) and Correlation
Coefficient were used to
evaluate the performance of
the proposed model against
three existing
prediction models.
Results showed that the
ANN model produced
accurate results in all four
provinces with minimum
error and best
correlation coefficient.
2019
[178]
To predict rain
attenuation using the
ANN model and
perform a comparison
with the ITU-R model.
The study utilized a
Percival disdrometer to
measure and record rain
rate data at a 1-min
integration time at
25 GHz.
A comparison between the
ANN model and the ITU-R
model was conducted.
Results showed the ANN
model performed better
than the ITU-R model.
2017
A 5-fold Cross-validation
approach was employed to
evaluate the model. However,
the RMS was calculated to
compare the model to
otherqmodels:
ρv = µ2v + σv2
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Table 19. Cont.
Ref.
Objectives
Methodology Adopted
Method of Validation
Comments/Findings
Year
[179]
To develop a neural
network-based rain
attenuation prediction
model (BPNN) that can
predict the rain rate
in advance.
The study utilized
4-year data obtained
using JW
RD-80 Disdrometer
measurements with a
sampling time of 30 s
which was used to train
and test the model.
The accuracy of the model
was evaluated in terms of the
Root Mean Square Error
(RMSE) and the Mean Square
Error (MSE) for different
rainfall events.
Error analysis results
produced a low value,
confirming that the
proposed BPNN model
can be trained and used
for rain attenuation
prediction.
2017
[180]
To propose new machine
learning methods using
KNN and ANN for
predicting short-term
rain attenuation for
ground wireless
communications.
The study utilized
time-series of rainfall
radar maps data
obtained from the JMA
webpage to train the
KNN and ANN objects.
Comparisons between the
actual rain rate and the
predicted rain rate, between
ANN and KNN in terms of
the total attenuation without
distance, and finally between
ANN and KNN in terms of
moderate rainfall.
Results showed that the
ANN method became less
accurate than the KNN
method after the
comparison without
distance, but both
methods performed better
than those proposed in the
literature.
2015
[181]
To propose two novel
rain attenuation
prediction models based
on BPNN and LS-SVM
algorithms for 60 GHz
millimeter wave.
The study randomly
selected samples from
experimental results
used previously in
research to establish a
relationship between the
rain intensity and rain
attenuation, excluding
other parameters.
A comparison between these
proposed models and the
ITU-R model was conducted
in terms of accuracy and
stability.
Results showed that
BPNN outperforms the
ITU-R model in terms of
accuracy and stability, but
the LS-SVM is a mode
ideal model for rain
attenuation prediction for
60 GHz frequency.
2013
[182]
To develop a method of
short-term prediction of
rain attenuation using
an ANN with a
self-adaptation
technique to varying
parameters.
This study utilized Ku
band data obtained from
3 different locations in
India for the testing and
validation of the model.
To evaluate the performance
of the model, a comparison
between the proposed model
and other short-term
prediction models was carried
out.
Results showed that the
accuracy decreases with
prediction interval but
remains within an
acceptable range.
2012
[183]
To develop an ANN
method based on the
extinction cross-section
data for rain attenuation
prediction in microwave
and millimeter wave
frequencies.
The study utilized
extension cross section
data obtained from
Modified
Prupacher-and-Pitter
(MPP) using the Finite
Element Method for
frequencies ranging
between 1–100 GHz.
The mean square error and
correlation coefficient were
used to evaluate the
performance of the developed
model.
Results showed that the
ANN produces accurate
results for estimating the
extension cross-section of
a raindrop, making it a
suitable tool for predicting
rain attenuation.
2008
To propose a new and
better rain attenuation
model known as
EPNet-evolved artificial
neural networks
(EPANN).
The study utilized data
obtained from the ITU-R
(CCIR) databank, which
contains earth-space rain
attenuation
measurement data
which was used to train
and test the proposed
model.
A comparison between the
proposed ANN and ITU-R
models was conducted in
terms of the prediction error.
Results showed that the
proposed model is
suitable for predicting rain
attenuation and performs
better than ANN and
ITU-R models.
2001
[184]
Findings from Table 19 indicate that machine learning models are simple and can
accurately predict rain attenuation. However, it can also be seen that the performance of
most of the machine learning-based models developed was evaluated against a statistical
model, the ITU-R model of which the ML-based model performs better.
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Table 20 presents the various machine learning-based models considered in the literature.
References
BPNN
SL-ANN
FFBNN
KNN
EPANN
LS-SVM
Linear Spline
Regression
CNN
LSTM
FFDTD
CFBP
AANN
EBP
SML
Table 20. Machine Learning-Based Models Considered in the Literature.
[184]
×
×
×
×
✓
×
×
×
×
×
×
×
×
×
[183]
×
[182]
×
[181]
✓
[180]
×
[179]
✓
[178]
×
[42]
[177]
[176]
×
×
×
[175]
×
[174]
✓
[96]
✓
[173]
×
[172]
×
[171]
✓
[170]
×
[169]
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✓
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✓
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From Table 20, it can be seen that only a few ML-based rain attenuation models have
been developed and evaluated; hence, there are still gaps to fill this research area. Figure 6
shows the taxonomy of the machine learning-based rain attenuation models considered in
the literature.
Aerial Communication
Unmanned Aerial Vehicles (UAVs), popularly known as drones, are self-contained and
can fly autonomously or be controlled by base stations. These autonomous node applications offer intriguing new approaches to completing a mission, whether related to military
or civilian operations such as remote sensing, managing wildlife, traffic monitoring, etc. [185].
UAV communication has become an integral part of the development of the 5G and beyond
network; however, one of the major application challenges faced by 5G and beyond UAV
communication is weather and climate change. In [186], aerial channel models, precisely
the air-to-ground channel models for different meteorological conditions such as rain, fog,
and snow were investigated within a frequency range from 2–900 GHz based on the specific
attenuation models for the different meteorology conditions. The results showed that rain
and snow are very severe for mm-wave and THz bands, respectively. The effect of rain on
the deployment of a UAV as an aerial base station in Malaysia was studied in [187] where
the antenna height of the user, attenuation due to rain, and high-frequency penetration loss
were considered for both the outdoor-to-outdoor and outdoor-to-indoor path loss models.
The study utilized two algorithms known as Particle Swarm Optimization (PSO) and Gradient Descent. The results obtained indicated that the PSO algorithm requires less iteration to
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converge compared to the GO algorithm and that the effect of rain attenuation increases for
higher frequency which results in a corresponding need for the UAV to increase its transmit
power by a factor of 4 and 15 for outdoor-to-outdoor and outdoor-to-indoor, respectively.
Figure 6. Taxonomy of the Machine Learning-Based Rain Attenuation Models.
9. Fade Mitigation Techniques for 5G
Fade mitigation techniques (FMTs) are adaptive communication systems employed
to correct in real time the effect of attenuation on slant path [188]. Fading has three major
effects: rapid fluctuations in signal strength over short distances or intervals, alterations in
signal frequency, and multiple signals arriving at different times. Signals are spread out
in time when they are put together at the antenna. This can result in signal smearing and
interference between received bits.
Due to high rainfall in tropical climates, a signal is attenuated, and this signal attenuation can be decreased utilizing FMT. To regulate FMT approaches in real-time, there
is the need to first understand the dynamic and statistical features of attenuation due to
rain, which is the major source of channel or path loss, especially when the frequency
exceeds 10 GHz [189]. Several methods to mitigate attenuation at the physical layer are
classified as Power Control, Adaptive Waveform, Diversity, and Layer 2. The power control,
adaptive waveforms, and Layer 2 techniques benefit from the system’s idle excess resources,
whereas the diversity technique uses a re-route method. With the sharing of idle resources,
the main aim is to make up for the fading of the link to sustain or optimize the performance.
The diversity technique, on the other hand, can preserve the performance of the link by
altering the geometry of the link or the frequency band [190].
9.1. Types of Fading
The different types of fading, as shown in Figure 7, are given considering the various
channel impairments and positions of the transmitter and receiver.
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Figure 7. Different Types of Fading.
9.1.1. Large-Scale Fading
Path loss produced by the impacts of the signal traveling over broad areas is referred
to as large-scale fading. The presence of noticeable topographical characteristics such as
mountains/hills, trees/forests, billboards, clumps of buildings, etc., between the transmitter
and receiver affects this phenomenon [191]. Path loss and shadowing effects are included
in large-scale fading.
A.
Path Loss
As signals propagate through the medium over a long distance, the signal strength
decreases with an increase in the distance. This is referred to as path loss or attenuation [151]. The amplitude of signals spreads as they propagate through the medium and,
if not compensated for, the signal would become unintelligible at the receiving end. This
loss is independent of the communicating parameters such as the transmitter, the type
of medium, or the receiver, although it can be mitigated by increasing the area of the
receiver’s capture [191].
B.
Shadowing
This refers to signal power loss caused by obstructions in the propagation route.
Shadowing effects can be used to reduce signal loss in various ways. One of the most
effective is LOS propagation. The EM wave frequency also affects shadowing losses. EM
waves can pass through different surfaces but lose power, i.e., signal attenuation. The type
of surface and the frequency of the signal determine the amount of loss. In general, as the
frequency increases, the penetration power of a signal decreases.
9.1.2. Small-Scale Fading
Small-scale fading describes the substantial variations in the phase and amplitude of a
signal that can occur due to minor variations in the spatial separation between a transmitter
and receiver [191]. Small-scale fading occurs when the intermediate components in the
signal’s path change. Multipath propagation, motion between sender and destination,
surrounding object speed, and signal transmission bandwidth are all physical elements that
cause small-scale fading. Small-scale fading in the radio propagation channel is influenced
by the physical causes highlighted below:
1.
Multipath propagation
This is one of the elements that contribute to radio signal deterioration. Because of
the irregularity in the atmosphere, the Point Radio Refractive Gradient (PRRG) varies with
height, time of day, and season [192]. As a result of this phenomenon, radio waves arrive at
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the receiving antenna via two or more routes. Because of this inter-symbol interference,
the time the signal takes to reach the destination becomes lengthened. Effects of multipath
include constructive interference, destructive interference, and signal phase shifting.
2.
Speed of the mobile
The effect of the various doppler shifts on multipath components is random frequency
modulation between the base station and the mobile. Doppler shift is positive when
receiving mobile travels toward the base station, and it is negative otherwise [192,193].
3.
Speed of surrounding objects
The effect of objects being in motion in the radio channel is a Doppler shift based on
varying times on the multipath components. However, if the neighboring objects move
faster than the mobile, this effect precedes fading [193].
4.
Transmission bandwidth of the signal
If the bandwidth of the transmitted signal exceeds the “bandwidth” of the multipath channel—which is measured by the coherence bandwidth—distortion will occur on
the receiving signal, although fading would not occur over a small distance. However,
if the bandwidth of the transmitted signal is lower than the bandwidth of the multipath
channel bandwidth, there would not be a distortion of the received signal, but its signal
power changes frequently. The coherence bandwidth, related to the channel’s unique
multipath structure, is used to quantify the channel’s bandwidth. Coherence bandwidth
is defined as the estimate of the maximum frequency for which the signal is in relation to
the amplitude [192].
The types of small-scale fading include the following:
A.
B.
C.
D.
Frequency selective fading: The signal is transmitted and received via multiple propagation paths, each with relative delay and amplitude variation. Multipath propagation
occurs when different regions of the transmitted signal spectrum are attenuated differentially, resulting in frequency selective fading. The channel spectral response is not
flat in this case, but exhibits dip or fade in response to reflections canceling particular
frequencies at the receiver.
Frequency non-selective fading: Frequency non-selective fading, also known as flat
fading, occurs when all signal component frequencies experience nearly the same
amount of fading. Such fading occurs when the transmitted signal’s bandwidth is less
than the channel’s coherence bandwidth. If the symbol period of the signal is greater
than the RMS delay spread of the channel, then the fading is flat.
Slow fading: Slow fading can occur as a result of occurrences such as shadowing,
which occurs when a significant object, for example, a mountain/hill or a billboard,
obstructs the path of the signal between the source and destination. It occurs over time
and alters the received signal mean value. It is mostly concerned with moving away
from the source and observing the estimated decrease in the intensity of the signal.
Fast fading: In this case, the signal suffers from frequency dispersion due to Doppler
spreading, which causes distortion. Fast fading is based on the speed of the mobile
and the bandwidth of the transmitted signal. Due to the rapid changes in the channel,
which is more than the signal period, the channel alters in one period.
9.2. Power Control Technique (PCT)
The PCT-fed mitigation concept is divided into four: (i) Up-Link Power Control
(ULPC), (ii) End-End Power Control (EEPC), (iii) Down-Link Power Control (DLPC),
and (iv) Onboard Beam Size (OBBS). However, in case the rain fade lasts a long time
and is expensive, then the power control strategy demands high power capacity because
the satellite transmitter that provides coverage to a diverse set of customers in various
geographical regions must continuously operate at, or close to, the maximum power to
mitigate the attenuation experienced by just one of the ground stations [190].
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9.3. Adaptive Waveform
The primary idea that regulates adaptive transmission is to maintain a constant Eb/No
by adjusting transmission parameters such as power level, symbol rate, modulation order,
coding rate/scheme, or any combination of these parameters [194]. Adaptive Waveform is
classified into three stages, which are: (i) Adaptive Coding (AC), (ii) Adaptive Modulation
(AM), and (iii) Data Rate Reduction (DRR) techniques [195–197].
9.4. Diversity Reception Techniques
The diversity reception technique compensates for fading channel impairments and is
typically achieved, for example, by using two or more receiving antennas. The diversity
technique is employed to mitigate or compensate for fades experienced by the receiver.
Base stations and mobile receivers can both use diverse approaches. These strategies aim to
reroute signals within the network to mitigate network disruptions caused by atmospheric
perturbation. Diversity is of three types: site diversity (SD), satellite diversity (SatD),
and frequency diversity (FD) [188]. These procedures are quite costly since the related
equipment must be redundant. Some of these techniques are applied [88] where a frequency
diversity model has been used to reduce signal attenuation in heavy rainfall zones. The FD
is used to overcome the rain fade in microwave point-to-point links as discussed in [198].
Figure 8 depicts the site diversity technique which consists of linking two or more ground
stations that are receiving the same signal so that if the signal is attenuated in one area,
another ground station can compensate for it.
Figure 8. Illustration of Site Diversity Scheme.
9.5. Frequency Variation Correction Factor
Frequency variation performance can be defined in terms of the outage percentage of
time [88]. The variation correction factor ( I )(𝐼)
[189,193] can be expressed mathematically as
shown in Equation (109):
P ( A)
I = 𝑃ND (𝐴)
(109)
P
𝐼 = WD ( A)
𝑃 (𝐴)
where PND ( A) denotes the outage percentage of an exact fade margin with no vari𝑃 (𝐴)
ation, and
PwD ( A) denotes the outage percentage with the same fade margin in the
𝑃
variation(𝐴)
frequency.
From Equation (109), the correction factor depends on the outage level at the required
attenuation, frequency separation, and diversity frequency. The specific attenuation for
the frequency is considered the starting point of the correction factor development [189].
A diversity correction factor model was proposed in [199] with a frequency separation
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of 5 GHz based on the fading margin required for system design. The result showed
a significant improvement within the frequencies ranging from 5 to 15 GHz, with no
improvement above 15 GHz. However, a model for any frequency separation and path
length for microwave communications is required.
9.6. Mitigation Techniques at Layer 2
At the layer 2 levels, FMTs do not try to mitigate a fade occurrence but instead rely
on message re-transmission. At layer 2, two distinct approaches are possible: Automatic
Repeat Request (ARQ) and Time Diversity (TD).
9.6.1. Automatic Repeat Request (ARQ)
ARQ is a data transmission error-control system that uses acknowledgments (or
negative acknowledgments) and timeouts to achieve reliable data transmission across an
unstable communication link. ARQ protocols are classified into three types: (i) Stop and
Wait ARQ, (ii) Selective Repeat ARQ, and (iii) Go-Back-N ARQ [200]. ARQ has a higher
spectral efficiency than repetition coding since it requires several transmissions only when
the first transmission happens in a severe fading state. However, the ARQ requires a
feedback channel because of the increased reliability requirements, which increases latency.
9.6.2. Time Diversity
In time diversity, signals of the same information are broadcast over the same channel
but within a time interval ∆t that exceeds the coherence time of the channel. The multiple
signals would be transmitted with a distinct fading condition, hence the diversity. Before
the transmission, a redundant error compensating code is inserted into the signal, which
is then spread over time using bit-interleaving. As a result, erroneous bursts are avoided,
simplifying error correction. This technique employs a propagation mid-term estimation
model to determine the best time to re-broadcast the signal without the need to repeat
the request [193,201].
Different types of fading mitigation techniques were identified and explained. It can
be seen that their principles of operations are different; however, they complement one
another. In some cases, hybridization is inevitable to improve availability/reliability,
capacity improvement, and limit interference when there is the need to mitigate high
impairments. Given these, Table 21 highlights some recent literature in these regards.
Table 21. Summary of Fade Mitigation Techniques.
Ref.
Objective of Study
Methodology Adopted
Result Obtained
Year
[197]
To develop an adaptive
coding-modulation scheme based on
a neuro-fuzzy system to achieve the
required BER performance and
channel data.
The study used MATLAB to
simulate and analyze the
neuro-fuzzy inference system to
choose the optimal
modulation-coding rate pair.
The results indicated that a system
with a low-order QAM scheme and
a low-convolutional coding rate is
efficient at sustaining the availability
of the link in severe tropical regions.
2022
[202]
Using the Macroscopic Diversity
Scheme, the work mitigates rain
attenuation for mmWave
(30–300 GHz) and THz frequency
(above 300 GHz).
The study used the DSD model to
estimate the attenuation due to
rain, and then employed the
macroscopic diversity technique to
mitigate it.
The study concluded by utilizing
macroscopic diversity. If a signal
from a hub is attenuated by rain,
a handover process takes place for
another hub that is not affected by
rain to take over the service.
2021
[203]
An adaptive per-link power control
strategy based on a
proportional-integral-derivative
(PID) controller was used to reduce
attenuation due to rain for a wireless
communication link.
The study used three separate
stations, each with MICAz as both
the transmitter and receiver, to
investigate the relationship
between power transmitted and
link quality.
Compared to other controller
systems, the PID controller provides
an optimal response for adaptive
power control due to its shorter
rising and setting time.
2019
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Table 21. Cont.
Ref.
Objective of Study
Methodology Adopted
Result Obtained
Year
[204]
To reduce attenuation due to rain
for a wireless fixed link in Port
Harcourt using the Adaptive
Power Control (APC) technique.
The study utilized 5-year
(2012–2016) rainfall readings for
modeling, and mitigating rain
attenuation analyzed using
MATLAB software.
Results after analysis showed that
2014 was the worst year for rainfall
with the highest attenuation, which
was successfully mitigated by the
ATPC technique.
2018
[205]
To mitigate rain attenuation for
12.255 GHz earth-to-satellite link
using time diversity technique
in Malaysia.
The research utilized 2-year data
collected using a 2.4 m-size
SUPERBIRD-C satellite transmitting
at a frequency of 12.255 GHz.
The results showed that the gain
recorded at 0.1% outage exceeded
6 dB, while at 0.01% outage the gain
exceeded 8 dB for a time delay of
10 min.
2018
[189]
To develop an estimation model
employing the frequency diversity
correction for FMT between
50 and 90 GHz.
The study employed the ITU-R
model to estimate the attenuation
due to rain based on the calculated
rainfall rate in the South-East Asia
tropical region.
The results indicated that the
improvement does not vary for
frequencies up to 70 GHz but
changes for frequencies above
70 GHz.
2017
[206]
To evaluate the influence of rain
on lower and higher operating
frequencies and to design a fade
mitigation technique known as a
switching circuit.
The study used a tipping bucket rain
gauge to collect 1-year rainfall data
utilizing an experimental link where
the transmitter and receiver operate
in two frequency bands, 5.8 GHz
and 26 GHz.
Results showed a negligible impact
of rainfall for the 5.8 GHz link,
whereas the effect is much stronger
for 26 GHz, hence switching to the
lower band during heavy rain.
2015
[199]
To propose and develop a
prediction model to reduce rain
fade between 5 and 40 GHz,
known as the frequency diversity
improvement factor.
The study employed the ITU-R
model to estimate the attenuation
due to rain using measured rain
rates in Malaysia.
Results showed that rapid
improvement was observed within
the frequency separation range of
5–15 GHz, but no improvement was
observed for separation above 15 GHz.
2015
9.7. Weakness of ITU-R Model for Rain Attenuation Research for 5G Networks and Beyond
The ITU-R model does not correctly predict rain attenuation over a short distance
for 5G network or beyond. As a result, the impacts of rain over short distances cannot
be accurately estimated using the conventional models that rely on the ITU-R model.
The inadequacy of the ITU-R model to accurately estimate the attenuation due to rain
along paths less than 2 km was shown in [71]. One such study in the literature showed the
evaluation of the ITU-R model in rain attenuation prediction in the 33–45 dB range [207].
The research was done in Budapest for a path length of 2.3 km at 72.56 GHz. The authors
demonstrated that the ITU-R model overestimated the attenuation when evaluated against
the measured attenuation. A comparable analysis predicted 26 and 38 GHz availability as
98.6% and 99.5%, respectively. An experimental investigation was conducted in Malaysia
at a distance of 0.3 km between source and destination, with predictions made using the
ITU-R model [43]. Further research has found that the ITU-R model has a larger prediction
error for distances less than 1 km [43,71,94,208–210]. Table 22 presents the summary of the
weakness of the ITUR-R model for short-distance applications.
Table 22. Summary of Works That Have Shown the Short-Distance Inability of the ITU-R Model.
Ref.
Location
Time
Frequency
(GHz)
Distance
(m)
General Comments/Findings
Year
[94]
Korea
3 yrs.
38/75
100
They developed a regression model to predict
attenuation at 75 Hz, and the model was benchmarked
with six existing models.
2017
[43]
Malaysia
1 yr.
26/38
300
This research utilized the effective distance to calculate
the error between measured and estimated attenuation.
2020
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Table 22. Cont.
Ref.
Location
Time
Frequency
(GHz)
Distance
(m)
General Comments/Findings
Year
[71]
Italy
4 months
73/83
325
The possibility of using existing models on attenuation
predictions on the E band has been analyzed.
2020
[20]
United
Kingdom
1 yr.
25.84/77.52
35
Effects of rain on building to building along with wet
antenna effects over a short range have been analyzed.
2019
[209]
Korea
1 yr.
73/83
500
The ITU-R model was deemed unsuitable in Korea
with a 100 mm/hr rain rate.
2013
[210]
Mexico
3 months
84
560
Many experiments were carried out to determine the
attenuation under standard conditions
2017
[208]
Japan
10 months
120
400
In this case, the results obtained agreed with each
other at a maximum rain rate (600 mm/hr).
2009
[211]
Czech
Republic
5 yrs.
58
850
It has been established that the annual average and
worst month of the year disagreed with attenuation
obtained by the ITU-R model.
2007
[212]
Albuquerque,
United States
of America
1.5 yrs.
72/84
1700
Techniques for determining specific attenuation were
presented since the ITU-R model inaccurately
predicted attenuation in the area.
2019
10. Future Research Directions
The section discusses further research directions for rain attenuation for 5G millimeterwave and briefly explains some of the industrial applications of the 5G technology.
10.1. Application of Machine Learning Techniques
The literature has shown that Artificial Intelligence (AI) is a very vast field that encompasses both machine learning (ML) and deep learning (DL), and also finds applications in
many areas of research, some of which are engineering, management, security, medicine,
science, environment, energy, and finance [213,214]. In the same way, AI-based models can
be employed to accurately predict rain attenuation, as well as mitigate it, in both satellite
and terrestrial communication links with minimum computations and errors [169–172].
Based on the review provided in Table 19, it can be seen that the performance of these
developed AI-based models was mostly evaluated against a statistical model—the ITU-R
model—and most of these models relied on temporal rain data and cannot generalize
large-scale systems [170]. Also, there are still few developed AI-based models, particularly
for the mitigation of rain attenuation for the 5G network and beyond. Therefore, for future
directions, the following are recommended:
1.
2.
3.
More novel AI-based models that can predict and particularly mitigate rain attenuation for the 5G network and beyond should be developed that are not solely based on
temporal rain data.
An efficient and simple AI-based path length reduction model should be developed
which can be used to determine the most appropriate path correction factor.
A novel and robust AI-based model should be proposed that can serve as a benchmark
for the evaluation of other newly developed AI-based models at all frequency and
rain rate ranges.
10.2. Regular Accessibility to Rain Data
It is known that rain behavior is changing due to climate change and weather conditions across the globe. Therefore, it is critical to establish a periodic check of the network
availability against the expected system design availability so that if the difference between
the predicted and the actual system attenuation is significant, then the system can be
modified for restoration to normal. In addition, if the method used for rain attenuation
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is dependent on a database, then there is a need to frequently update the database with
the most recent rain rate data. However, one of the major challenges with this periodic
checking is the cost; therefore, for future work a cost-effective and easy way to generate
and collect rain data should be proposed.
10.3. Rain Attenuation Research for 5G and beyond UAV Communication Network
The Unmanned Aerial Vehicle (UAV) has been proven to be an integral part of the development of the 5G network and beyond; however, rain is one of the major meteorological
conditions that affects UAV communication, especially for mm-wave. Furthermore, there
is very little research on how weather and climate conditions can affect 5G and beyond
UAV communication networks as it is difficult to meet QoS requirements under dynamic
environments with varying locations. Further work recommends that adaptive beamforming techniques for UAV communication for both millimeter and THz bands considering
the effects of various meteorological conditions should be studied and models that can
accurately predict and mitigate these effects should be developed.
11. Conclusions
Rain is a significant source of attenuation for electromagnetic wave propagation, particularly when the frequency range exceeds 10 GHz. This paper conducted a systematic review
of research efforts on rain attenuation models. Some of these investigations have resulted
in novel findings and models. According to the study, existing rain attenuation prediction
models have been classified as empirical, statistical, physical, fade-slope, and optimizationbased models. It can be seen that, although the Crane and ITU-R models are the most
widely used models for rain attenuation prediction, they under- or overestimate the attenuation in tropical regions. Hence, none of the existing models can accommodate all
the environmental factors considered in the design of a wireless network. There is a need
for more research in different environments. Also, RMS is the most widely celebrated
method for testing the accuracy of the developed rain attenuation models for different
environments. However, other methods that have not been given the expected attention
are still available. This study also examined existing fading mitigation approaches where it
was seen that the adaptive waveform techniques are the most utilized method. Machine
learning-based models were also presented and from the review, it can be seen that this
research area still has many gaps to fill in terms of developing a model to accurately predict,
and particularly to mitigate rain attenuation. Moreover, other areas of further research that
could assist global communities to achieve higher penetrations of the new technology were
highlighted. If all of these are given the expected attention, there is room for further improvement in cost, reliability, and energy consumption for future communication networks.
This study can serve as reference material for network designers and for new and existing
researchers to enhance their skills in developing 5G and beyond 5G wireless networks.
Author Contributions: The manuscript was written through the contributions of all authors. Conceptualization, E.A., A.A., I.A., A.D.U., and N.F.; methodology, I.-F.Y.O., K.S.A., A.A.O., and H.C.;
software, O.A.S., L.A.O., S.G., and A.L.I.; validation, A.M., Y.A.A., and L.S.T.; formal analysis, E.A.,
A.A., I.A., A.D.U., and N.F.; investigation, I.-F.Y.O., K.S.A., A.A.O., and H.C.; resources, A.M., Y.A.A.,
and L.S.T.; data curation, O.A.S., L.A.O., S.G., and A.L.I.; writing—original draft preparation, E.A.,
A.A., I.A., A.D.U., and N.F.; writing—review and editing, O.A.S., L.A.O., S.G., and A.L.I.; visualization, A.M., Y.A.A., and L.S.T.; supervision, N.F.; project administration, Y.A.A.; funding acquisition,
N.F. All authors have read and agreed to the published version of the manuscript.
Funding: This work is funded by the Federal Republic of Nigeria under the National Research Fund
(NRF) of the Tertiary Education Trust Fund (TETFund) Grant No. TETF/ES/DR&D-CE/NRF2020/
SETI/64/VOL.1 and by the Nigeria Communications Commission (NCC) under Grant No. NCC/R&D/
RG/SLU/001.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
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Data Availability Statement: Not applicable.
Acknowledgments: The work of Agbotiname Lucky Imoize is supported in part by the Nigerian
Petroleum Technology Development Fund (PTDF) and in part by the German Academic Exchange
Service (DAAD) through the Nigerian-German Postgraduate Program under grant 57473408.
Conflicts of Interest: The authors declare no conflict of interest related to this work.
Abbreviations
AC
ACK
ACM
AM
ARQ
ARS
BER
BPNN
CCDF
CDF
CI
CMN
CRAN
CRC
CV
DBSG3
DEA
DLPC
DRR
DSD
DVD
EEPC
EIRP
FD
FEC
FMT
GP
GPCC
GRSME
ITU
IDW
JW
LMDS
LOS
mmWave
MPM
NACK
NASA
NCC
NLOS
NOAA
OBBS
PCT
PL
PRRG
QAM
QNMRN
RMS
RMSE
Adaptive Coding
Acknowledgement
Adaptive Coding and Modulation
Adaptive Modulation
Automatic Repeat Request
Average Raindrop Size
Bit Error Rate
Back-Propagation Neural Network
Complementary Cumulative Distribution Function
Cumulative Distribution Function
Close-In
Commercial Microwave Network
Centralized Radio Access Network
Cyclic Redundancy Check
Convective
Databank Study Group 3
Differential Evolution Approach
Downlink Power Control
Data Rate Reduction
Raindrop Size Distribution
Dimensional Video Disdrometer
End-to-End Power Control
Effective Isotropic Radiated Power
Frequency Diversity
Forward Error Correction
Fade Mitigation Technique
Gaussian Process
Global Precipitation Climatology Centre
Gaussian Root Mean Square Error
International Telecommunication Union
Inverse Distance Weighting
Joss Waldvögel
Local Multipoint Distributed Service
Line of Sight
Millimeter-Wave
Millimeter-Wave Propagation Model
Negative Acknowledgement
National Aeronautics and Space Administration
Nigeria Communications Commission
Non-Line of Sight
National Oceanic and Atmospheric Administration
Onboard Beam Shaping
Power Control Technique
Path Length
Point Radio Refractive Gradient
Quadrature Amplitude Modulation
Quasi-Newton Multiple Regression
Root Mean Square
Root Mean Square Error
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SatD
SC-RSME
SD
SL-ANN
SML
SST
ST
TRMM
ULPC
Symbols
a and b
A
ar
Acl
Adry
Ae f
A gs
AnL
Aox
Ap
Arad
Awet
Awv
Cn
dc
dr
dw
D
E
EE (∧)
Ev (v)
ER ( j )
Ej
EM
ET
f
Fi
f r pri
f rsec
fs
g
Grx
Gtx
hox
hwv
I
Ifγ
j
Lc
LD
Leq
Lp
LT
Lwc
K
Kl
ko
M
Mc
Satellite Diversity
Spread-Corrected Root Square Mean Error
Site Diversity
Single-Layer Artificial Neural Network
Supervised Machine Learning
Synthetic Storm Technique
Stratiform
Tropical Rainfall Measuring Mission
Uplink Power Control
Functions of frequency
Rain attenuation
Average rain rate
Attenuation due to cloud
Losses due to path
Effective aperture area
Total attenuation due to water vapor and oxygen
Attenuation loss due to non-line of sight
Attenuation due to dry air (oxygen)
Rain attenuation exceeded at p% of the time
Attenuation due to radome
Losses due to rain
Attenuation due to water vapor
Interpolation constant
Cell diameter
Physical thickness of radome
Physical thickness of water layer
Rain drop size
Mean error
Electron energy of the molecule
Vibrational energy
Rotational energy
Expected count in a cell j
Energy of the molecules
Translational motion energy
Frequency
Oxygen or water vapor line shape factor
Principal relaxation frequency
Secondary relaxation frequency
Fade-slope
Gravitational acceleration
Receiving antenna gain
Transmitting antenna gain
Equivalent height for dry air
Equivalent height for water vapor
Variation correction factor
Proposed increment factor
Imaginary unit
Path length of the cell
Path length of the debris
Equivalent propagation path length
Path length
Actual path length
Liquid water content
Constant of proportionality
Cloud liquid water-specific attenuation coefficient
Free-space wavenumber
Amount of information in the measurement set index
Mie’s Coefficient
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n
N
N ( D, R)
′′
Nox ( f )
′′
Nwv ( f )
′′
ND ( f )
Oj
p
ptot
P( A)
Pc
PD
Pd
Pm
PND ( A)
PWD ( A)
P(γ)
r
r av
Rc
RD
Re
Rf
Ri
Rm
Rm
Rp
Rp
Rp
R pk
Rr
Si
T
Tatt
tp
tw
x (t)
Wc
WD
wi
v
Greek Letters
∂
ð
ε
εp
ε( p)T
ξd
η
θ
σD
σ fs
λ
P
ρ
ρd
ρdo
τw
Generation index
Number of rain gauges
Distribution for rain drop size
Imaginary part of the frequency-dependent complex refractivity for oxygen
Imaginary part of the frequency-dependent complex refractivity for water vapor
Dry continuum due to pressure-induced nitrogen absorption and the Debye spectrum
Observed count in a cell j
Pressure
Total barometric pressure
Cumulative probability of attenuation
Probability of a cell
Probability of debris
Power density
Mutation variable
Outage percentage of an exact fade margin with no variation
Outage percentage with the same fade margin in the variation frequency
Probability that specific attenuation is exceeded
Path reduction factor
Average path reduction factor
Rain rate for the cell
Rain rate for the debris
Effective rain rate for terrestrial links
Rainfall
Weighted sum of the rain gauges values
Measured rain rate
Mean measured rain rate
Rain rate exceeded at %p of the time
Predicted rain rate
Mean predicted rain rate
Peak intensity
Rain rate
Strength of the ith oxygen or water vapor line
Temperature
Total attenuation
Prediction time
Thickness of water layer
Percentage of time
Length scale for the cell
Length scale for the debris
Weight of each rain gauge value
Photon frequency
Width parameter for the Debye spectrum
Radius of the radome
Complex dielectric permittivity constant
Relative error margin
Goodness-of-fit function
Droplet’s complex permittivity
Normal distribution function
Elevation angle
Standard deviation of the natural logarithm of the rain rate
Fade-slope standard deviation
Wavelength
Conditional distribution of the fade-slope
Rank correlation
Distance from the center
Conditional average radius
Electrical thickness of water layer
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τr
µk
X
γ
γa
γc
γh,v
γo
γw
Electrical thickness of radome
Kinematic viscosity of water
Pearson goodness fit function
Specific rain attenuation
Average specific attenuation
Cloud-specific attenuation coefficient
Specific rain attenuation for vertical and horizontal polarization
Specific attenuation due to oxygen
Specific attenuation due to water vapor
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