Estimation of satellite link’s fade margin using non-meteorological technique and worst month analysis

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4136~4144
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4136-4144  4136
Journal homepage: http://ijece.iaescore.com
Estimation of satellite link’s fade margin using
non-meteorological technique and worst month analysis
Nur Hanis Sabrina Suhaimi1,2
, Ahmad Fadzil Ismail2
, Khairayu Badron2
, Yasser Asrul Ahmad2
1
Submarine Technology Branch, Maritime Technology Division, Science and Technology Research Institute for Defence,
Perak, Malaysia
2
Department of Electrical and Computer, Kulliyyah of Engineering, International Islamic University Malaysia, Selangor, Malaysia
Article Info ABSTRACT
Article history:
Received Jun 8, 2022
Revised Oct 27, 2022
Accepted Nov 6, 2022
Satellite technology is shifting to higher frequencies such as Q or V-band to
cater to greater bandwidth and higher data rates applications such as
videoconferencing, internet of things (IoT) and telemedicine. The main
challenge in deploying high-frequency bands in heavy precipitation areas is
severe rain attenuation. In this paper, a frequency scaling technique was
developed to estimate the fade margin at a higher frequency. The worst
month analysis was also conducted since the analysis is also important in
determining dependable fade margin. The result was evaluated and analyzed
using root mean square error (RMSE) and percentage error. The proposed
model offers the smallest RMSE and lowest percentage error when
compared to all existing prediction models. A dependable fade margin
acquired from high-accuracy rain attenuation estimation is very important.
This is to apply the best mitigation technique in overcoming rain attenuation
in the satellite-Earth link so that, the best system performance can be
delivered.
Keywords:
Frequency scaling
Rain attenuation
Satellite-earth link
Tropical region
Worst month
This is an open access article under the CC BY-SA license.
Corresponding Author:
Nur Hanis Sabrina Suhaimi
Submarine Technology Branch, Maritime Technology Division, Science and Technology Research
Institute for Defence
RMN Navy Base, Lumut, Perak 32100, Malaysia
Email: hanis.sabrinasuhaimi@gmail.com
1. INTRODUCTION
In recent years, an application that utilizes the internet and data-driven technology leads to
the invention of high throughput satellites (HTS). HTS offers a high throughput and greater capacity which
can reduce cost-per-bit whereas, in Malaysia, high-speed broadband is provided by MEASAT 5 [1].
Currently, most of the HTS works in Ka-band as Ka-band has larger bandwidth compared to C and Ku-band.
No doubt, the satellite service provider will shift to higher frequencies such as Q or V-band in the future as
greater bandwidth and larger capacity demand increase [2]–[6]. However, higher frequencies such as
millimeter-wave frequencies are very vulnerable and susceptible to the scintillation effect, especially in rain
[7], [8]. The rain will absorb and scatter the signal due to its shorter wavelength characteristics [9]–[12].
Since Malaysia is located in a tropical region which experiences severe rain and has a high-humidity
environment, rain attenuation is the main challenge in applying higher frequencies technology [13]. Rain
attenuation caused signal degradation that decreases the quality of service (QoS) [14], [15]. To attain a good
QoS and reliable signal availability for satellite link transmission, proper planning is required to mitigate
the attenuation due to rain effects at higher frequency bands.
Frequency scaling is a non-meteorological technique that can predict rain attenuation concerning
frequency. The attenuation at desired frequencies can be predicted if rain attenuation at one reference

Int J Elec & Comp Eng ISSN: 2088-8708 
Estimation of satellite link’s fade margin using non-meteorological … (Nur Hanis Sabrina Suhaimi)
4137
frequency at a particular area is known [7], [16]–[18]. The frequency scaling method is an alternative method
to predict rain attenuation models, where rainfall data is not available. Reliable long-term rain attenuation
data in tropical regions is limited. This leads to the derivation of the frequency scaling technique [9]. The
frequency scaling technique is one scaling technique that also can be extended to other scaling techniques
which are polarization scaling, elevation angle scaling, and pathlength scaling as the attenuation is a function
of the path length, elevation angle, and frequency [19]. In the frequency scaling technique, f1 is referring to
known frequencies and is usually lower than desired frequency which is f2 [20]. Two approaches are available
in deploying frequency scaling techniques which are long-term frequency scaling and short-term frequency
scaling. Long-term frequency scaling or statistical frequency scaling highlights the relationship of
attenuations at identical probability levels [16], [18]. It helps to define attenuation in a high-frequency band
using measurements performed in a lower-frequency band. Power control also can be estimated by using this
approach to compensate for uplink attenuation by using measured downlink attenuation. Short-term
frequency scaling, well known as instantaneous frequency scaling, defines the relationship between
simultaneous attenuation at different frequencies [18]. This approach can be used to generate attenuation time
series at a high frequency by using attenuation time series measured at a lower frequency. This method can
improve uplink power control in terms of real-time estimations.
This paper focuses on the first method which is when only attenuation information on known
frequency is provided. Frequency scaling is derived from the empirical formula in terms of the attenuation
ratio of desired and known frequency directly as a function of frequency and attenuation. The frequency
scaling technique can be applied on the same path as the same path experience the same rainfall rate.
Attenuation is directly proportional to the rainfall rate. Frequency scaling of attenuation is preferable deriving
from long-term attenuation data instead long-term rain data [20]. Thorough studies on frequency scaling
techniques in tropical regions are required since the ITU-R proposed frequency scaling that derives based on
temperate region data. The temperate region experiences less precipitation compared to the tropical region,
thus the frequency scaling proposed by ITU-R is less accurate to be used in Malaysia. Previous researchers
have an effort in carrying out the frequency scaling using the empirical frequency scaling ratio but they are
focusing on instantaneous frequency scaling instead of statistical frequency scaling.
In this paper, the existing frequency scaling models and the proposed method were compared with
the measured data. The newly derived frequency scaling technique from empirical data on the rainy path was
developed in this paper. Its performance was evaluated under a different year of data with different rain
conditions within the same location in tropical regions. This paper also highlighted the worst month analysis.
The satellite system planning often requires the attenuation value exceeded for a time percentage, pw, of the
worst month. The procedure used to estimate the attenuation exceeded for a specified percentage of the worst
month in tropical regions was elaborated in the next section.
2. METHOD
The beacon signal data from 12.201 GHz of Ku-band was retrieved from the MEASAT-3 satellite
receiver and 20 GHz of Ka-band from the MEASAT-5 satellite receiver. The data was collected for one year
which is from January 2016 to December 2016. Both receivers are located at Earth stations in Cyberjaya,
Malaysia (N 2.9350°, E 101.6580°). The MEASAT-3 satellites are located in an orbit at 91.5° E and
the MEASAT-5 is located in an orbit at 119.5° E. The rainfall data were retrieved from a rain sensor installed
at ASTRO Broadcast Centre. The 8.1 m dual reflector Gregorian antenna was used to receive the Ka-band
beacon signal and the Ku-band antenna was a Cassegrain type with a diameter of 13.1 m. The elevation
angles are 77.5 degrees and 68.8 degrees for the MEASAT-3 and MEASAT-5 satellites respectively.
The received beacons at the antenna were amplified by a low noise amplifier (LNA). Then, the beacons were
down-converted to 1.5 GHz. The digital signal processing (DSP) based tracking receiver used a frequency
of 1.5 GHz as input for satellite tracking and uplink power control. The received power signal of Ku and
Ka-band during the non-rainy and rainy days were compared and analyzed to derive attenuation values.
The attenuation values were derived by deducting received power signals during rainy days with
clear sky values. The clear sky value was determined from the average minimum received signal throughout
the year. The monthly rain attenuation was accumulated and processed to obtain the cumulative distribution
function (CDF). The CDF was represented in the time exceedance percentage graph. The time exceedance
percentage graph is crucial for satellite engineers since it represents the percentage of link availability. The
required fade margin can be determined from the annual CDF of each frequency. Figure 1 shows the annual
CDF of Ku-band and Ka-band rain attenuation compared with the ITU-R P. 618-13 general prediction as
well as ITU-R P. 618-13 frequency scaling method. From the Figure 1, at 0.01% of the time exceedance, rain
attenuation of Ku and Ka-band predicted by the ITU-R have smaller values (15.7 and 42.5 dB) compared to
the measured attenuation values which are 32 and 46.9 dB. When applying the frequency scaling model

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proposed by ITU-R, at 0.01% of time exceedance, rain attenuation for the Ka-band is 70.8 dB which is
overestimating the measured rain attenuation.
Figure 1. Comparison between empirical measured data and ITU-R P. 618-13 at Ku and Ka-band link
2.1. Frequency scaling technique
Several frequency scaling approaches from previous studies were tested for the Ka-band along the
same communication paths. The simple power-law model is one of the well-known frequency scaling
models. The model applies statistical attenuation ratio (RAS) as in (1).
𝑅𝐴𝑆𝑛 = (𝐴(𝑓2))/(𝐴(𝑓1)) = (𝑓2/𝑓1)𝑛 (1)
Various values of power n have been proposed by [21]: n=1.8 Dintelman, n=2 Owalabi and Ajayi, n=1.9
Olympus, and n=1.72 Drufuca. The model has been proposed by ITU-R [20] is
𝐴2 = (𝐴1(𝜑₂/𝜑₁)(1−𝐻(𝜑(𝑓1),𝜑(𝑓2),𝐴1))
(2)
𝜑(𝑓) = 𝑓2/((1 + 10−4
) × 𝑓2
) (2a)
𝐻(𝜑₁, 𝜑₂, 𝐴1) = 1.12𝑥10−3
× (𝜑₂/𝜑₁)0.5
(𝜑₁ × 𝐴1)0.55
. (2b)
Boithias [21] proposed an improved frequency scaling model which is:
𝐴2/𝐴1 = (𝜑2/𝜑1)(1−𝐻(𝜑(1),𝜑(2),𝐴1))
(3)
𝜑(𝑓) = 𝑓2
/(1 + 10−4
× 𝑓2
) (3a)
𝐻(𝜑1, 𝜑2, 𝐴1) = 1.12 × 10−3
× (𝜑2/𝜑1) × (𝜑2 × 𝐴1)0.55
. (3b)
A simple model has been proposed by CCIR [17] is:
𝑅𝐴𝑆𝐶𝐶𝐼𝑅 = 𝐴𝑓2/𝐴𝑓1 = 𝜑(𝑓2)/𝜑(𝑓2) (4)
𝜑(𝑓) = 𝑓1.72
/(1 + 3 × 10−7
× 𝑓 3.44
). (4a)
The more complex models proposed by Hodge, Battesti, and Kheirallah are in (5) to (7), respectively [17].
𝐴2/𝐴1 = (𝑎2/𝑎1)(𝐴1/𝑎1 × (√(𝑏1/𝜋)))(𝑏2/𝑏1)−1)
× (√(𝑏1/𝑏2)) (5)
If 𝑓1, 𝑓2 ≤ 20 𝐺𝐻𝑧, 𝐴𝑓2/𝐴𝑓1 = (𝑓2 − 6)/(𝑓1 − 6) (6)
If 𝑓1, 𝑓2 ≥ 20 𝐺𝐻𝑧, 𝐴𝑓2/𝐴𝑓1 = (𝑓2 − 10)/(𝑓1 − 10) (6a)
If 𝑓1 < 20 𝐺𝐻𝑧, 𝑓2 > 20 𝐺𝐻𝑧, 𝐴𝑓2/𝐴𝑓1 = 1.4 × (𝑓2 − 10)/(𝑓1 − 6) (6b)

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𝐴𝑓2/𝐴𝑓1 = (𝐴𝑠2 × 𝐿2 × 𝑟2)/(𝐴𝑠1 × 𝐿1 × 𝑟1) = 𝐴𝑠2/𝐴𝑓1 (7)
Zhou [22] has proposed a model that was tested in Singapore which is:
𝐴2 = 4.8 + 1.61 × 𝐴1. (8)
2.2. Worst month analysis
The worst-month analysis is very useful to design satellite-to-Earth because the required fade
margin must be contented in any given month throughout the year. Therefore, performing analysis for the
worst month data is crucial for telecommunication system designers [23]–[26]. The worst month statistic is
determined by choosing the worst at the occurrence level computed during 12 sequential months. Comparing
each monthly CDF at a particular level is recognized as the worst month [5], [23], [24]. In this paper, at
probability exceeding 0.1% and 0.01% of the time outage percentage, January and May 2016 are the worst
months for the year 2016 of Ku-band fading and March 2016 is the worst month for the year 2016 of
Ka-band fading. The relationship between worst-month and annual statistics is defined as (9):
𝑄 = 𝑋/𝑌 (9)
where X is the average worst-month probability and Y is the average annual probability for the same
threshold. Regions with the same climatic categories will have alike values of Q. Least square method is one
of the methods to obtain regression parameters Q1 and β with the measured rainfall rate and rain attenuation
data. ITU-R has proposed to relate the parameters Q1 and β by a power law as in (10) and (11).
𝑄 = 𝑄1 𝑌−𝛽
(10)
𝑋 = 𝑄1 𝑌1−𝛽
(11)
3. RESULTS AND DISCUSSION
3.1. Frequency scaling
All the existing frequency scaling models produce variations of attenuation values since the models
were derived in different locations. Different regions have different geographical climates and rain statistics.
Rain statistics also vary with drop size distribution, temperature, and rain height. All the estimation models
are higher than the measured values. The previous models of frequency scaling were compared with the
measured Ka-band rain attenuation as presented in Figure 2.
Figure 2. Comparing the previous model of frequency scaling with measured Ka-band rain attenuation value

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From the Figure 2, it can be seen at 0.01% of time exceedance, the rain attenuation prediction from
all available models including the ITU-R gives larger values compared to the measured value of Ka-band rain
attenuation. This paper proposed a new model of frequency scaling that is useful for the tropical region. The
model utilized Ku-band rain attenuation data at Cyberjaya as an attenuation at a base frequency to determine
attenuation at the desired frequency at the Ka-band link. The proposed frequency scaling of rain attenuation
for tropical regions is proposed by given in (9), (9a), and (9b).
𝐴2 = (𝛼 × 𝐴1
0.669
) − (1.425 × 𝑙𝑛(𝑏) + (2 × 𝑏)) (9)
𝑏 = 𝑓2/𝑓1 (9a)
𝛼 = (√(𝑓2 − 𝑓1)) + 1.38 (9b)
A1 is Attenuation at the upper frequency, A2 is Attenuation at the lower frequency, f1 is the lower frequency,
and f2 is the upper frequency.
Rain attenuation prediction by applying the frequency scaling method such a best alternative way to
determine rain attenuation at the desired frequency when the rainfall data is not available. A reliable fade
margin is obtained from a rain attenuation prediction model that has high accuracy. The proposed frequency
scaling model for year-wise estimation of rain attenuation for the Ka-band compared with the measured
Ka-band rain attenuation is depicted in Figure 3. From the figure, it can be seen that the proposed model fits
the measured rain attenuation of the Ka-band compared to other previous models. From the figure, fade
margins at 0.1%, 0.3%, 0.01%, and 0.03% of time exceedance were determined. Table 1 shows the fade
margin obtained from measured and predicted Ka-band rain attenuation in tropical regions. From the table,
it is noted that the percentage error at all selected time exceedance was below 20% which has high accuracy.
The fade margin obtained for the Ka-band rain attenuation prediction model at 0.01% of time exceedance is
51 dB.
Figure 3. Comparing the proposed frequency scaling model with measured Ka-band rain attenuation
Table 1. Fade margin value of Ka-band rain attenuation at 0.1%, 0.3%, 0.01% and 0.03% of time exceedance
Time Exceedance (%) Measured Value of
Rain Attenuation (dB)
Predicted Value Using
New Model (dB)
Percentage Error
0.3 9 9 0
0.1 25 22 12
0.03 37 32 13.5
0.01 52 51 1.9
All the previous models and the new models were analyzed and evaluated by using percentage error
and root mean square error (RMSE). Table 2 shows the error matrix consisting of RMSE value and average
relative percentage error comparing previous models and the proposed model with measured rain attenuation
at the Ka-band link. At the same probability level, the percentage errors, ε, between measured rain
attenuation data (A%p, measured) and the model’s predictions (A%p, predicted) were evaluated within the
percentage interval of 0.001%<P<1%. The percentage error was determined by using (10).
Percentage error, ε = (A% predicted– A% measured/(A% measured × 100 [%] (10)

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Table 2. Percentage error obtained after testing over the interval [0.001% to 1%]
No Model RMSE ε
1 Dintelman 29.0 32.3
2 Owalabi 35.9 39.6
3 Drufuca 26.5 30.9
4 Olympus Data 32.4 35.7
5 Hodge 13.2 25.6
6 Battesti 24.5 30.1
7 Kheirallah 8.5 30.4
8 CCIR 26.5 30.9
9 Boithias 5.0 28.2
10 Zhou 9.9 17.3
11 FS ITU-R 28.3 28.0
12 Proposed FS 2.8 11.3
As presented in Table 2, the proposed frequency scaling model gives the smallest RMSE value and
lowest average relative error percentage as compared to the previous models for 0.001%<P≤1% of time
exceedance. The value of RMSE for the proposed model is 2.8 and the average relative error percentage of
11.3%. Lower the RMSE, the model better fits the measured data. Thus, this result proved that the model is
more practical to be used in tropical regions such as Malaysia. The frequency scaling model proposed by
ITU-R is inaccurate to be applied in the tropical region since it has a high RMSE value and huge percentage
error which are 28.3 and 28% respectively.
3.2. Worst month
The ITU-R claims that standards value for parameters Q1=2.85 and β=0.13 can be used for any
planning globally and parameter values of Ql=1.7 and β=0.22 are proposed for Indonesia [25]. The worst
month analysis for Ku-band at Cyberjaya in the year 2016 was detected in May 2016 as shown in Figure 4.
The value of Ql=1.85 and β=0.18 for rain attenuation at the Ku-band link and of Ql=1.67 and β=0.21 for rain
rate as shown in Figure 5(a). Figure 5(b) presented four comparison graphs which are CDF of rain
attenuation cumulative distributions for the year 2016, the proposed worst month envelope that was acquired,
the measured worst month from annual CDF rain attenuation and the predicted average worst month
proposed by ITU-R.
Table 3 summarizes the Q1 and β parameter values at Cyberjaya with other tropical locations as well
as compares to ITU-R proposed values. The measured value of Q1 and β for Cyberjaya is close to the
proposed value by ITU for Indonesia since Indonesia and Malaysia are located in tropical and equatorial
regions. The Q factor is climatic dependent, thus different climate regions will have different Q factors.
However, in this paper, a new value of parameters Q1 and β has been proposed created on the Cyberjaya
weather information since different places provide a different value of Q1 and β. Besides that, for the worst
month analysis for Ka-band rain attenuation, the monthly and yearly distributions only up to 0.1% of time
outage since the attenuation or Ka-band is saturated at 33 dB. Therefore, accurate parameters Q1 and β cannot
be properly defined. Measured values of Q1 and β for Ka-band rain attenuation are Ql=2.19 and β=0.0007 is
not close to the worst month proposed by ITU-R for the Indonesia region.
Figure 4. Specific threshold of rain attenuation at Ku-band for the year 2016 at Cyberjaya

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(a)
(b)
Figure 5. The worst month of rain attenuation (a) a regression fit to determine Ql and β of the worst month of
Ku-band rain attenuation and (b) the worst month’s model comparison
Table 3. Measured values and existing proposed values for Q1 and β in the tropical region
Q1 β
Islam et al. [17] 1.32 0.27
Islam et al. [26] 0.92 0.3
Ting and Mandeep [2] 1.68 0.7
ITU-R for Indonesia [20] 1.7 0.22
ITU-R for a general purpose [20] 2.85 0.13
Cyberjaya (Ku-band attenuation) 1.85 0.18
4. CONCLUSION
Since Malaysia is located in a severe rainfall geographical country, an analysis of the frequency
scaling technique of the satellite-Earth link as well as the worst month analysis has been conducted. The
analysis is very important in determining a reliable fade margin for Ka-band rain attenuation. This paper has
presented the results on the annual cumulative distribution of Ka-band rain attenuation compared to the
ITU-R prediction model as well as the ITU-R frequency scaling model. The new frequency scaling formula
was proposed and the ratio of exceedances between the average year and the average worst month, Q in the
tropical region also was discussed. In a conclusion, the proposed frequency scaling model gives a small
RMSE and low percentage error compared to the existing frequency scaling model. The fade margin of
Ka-band rain attenuation obtained from the new model and measured values are 51 and 52 dB, respectively.

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ACKNOWLEDGEMENTS
The authors would like to express acknowledgement towards MEASAT Satellite System Sdn Bhd
(MEASAT), ASTRO Broadcast Centre (ASTRO) and the Department of Irrigation and Drainage (DID),
Malaysia for providing data for this research project. This research was supported by a Grant of
P-RIGS18-005-0005.
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BIOGRAPHIES OF AUTHORS
Nur Hanis Sabrina Suhaimi obtained her B.Eng. in communication engineering
from International Islamic University Malaysia (IIUM) in 2013, and a master’s in remote
sensing and GIS from the University Putra Malaysia (UPM) in 2015. She is currently one of
the research officers at the Science and Technology Research Institute of Defence (STRIDE)
and also pursuing her Ph.D. in engineering at IIUM in the field of Satellite-to-Earth rain
attenuation. Previously, she had experience working as Software Engineer at SONY(M)
EMCS SDN BHD. Currently, she is working in electronic and communication systems for the
submarine. She can be contacted at email: sabrina.suhaimi@stride.gov.my.
Ahmad Fadzil Ismail is one of the professors at the International Islamic
University Malaysia (IIUM). He received his degree at Gannon University, Pennsylvania,
USA with Cum Laude Latin honours. He possesses his master’s and Ph.D. from the University
of Essex, University of Bath, UK. He is one of the Professional Engineer BEM and a member
of IEEE. Previously, he also managed to supervise and perform installations and
commissioning of the Integrated Communication Systems for 4 Kedah-Class Royal Malaysian
Navy’s Next Generation Patrol Vessels at Boustead naval shipyard in Lumut, Perak. His
research fields comprise satellite communications, radio frequency, wireless systems, active
and passive target tracking algorithms, as well as microwave and millimetre-wave technology.
He can be contacted at email: af_ismail@iium.edu.my.
Khairayu Badron acquired her degree, master’s, and Ph.D. from International
Islamic University Malaysia (IIUM) in 2007, 2011, and 2016. At present, she is a lecturer at
the Faculty of Engineering, IIUM. Her research interests are in satellite communication and
navigation, propagation effects on microwave and millimetre links, as well as radar
radiometry. She is very active as now she is one of the members of IEM and BEM. She is also
a member of MTSFB and Perintis. She actively published and co-authored papers in indexed
journals as well as conferences on subject interest. She can be contacted at email:
khairayu@iium.edu.my.
Yasser Asrul Ahmad obtained his degree from Vanderbilt University, USA. He
received his master’s from the University Kebangsaan Malaysia (UKM) and Ph.D. from
International Islamic University Malaysia (IIUM). Presently, he is one of the lecturers at
IIUM. Before this, he had an 11-year career with Astronautic Technology Sdn Bhd, he served
as the project manager, system engineer, and telecommunication engineer. His research fields
are nanosatellite development and satellite communications. He is one of the members of IEM
and a Professional Engineer of BEM. He can be contacted at email: yasser@iium.edu.my.

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