Chapter 9
Atmospheric Propagation Model for Satellite
Communications
Ali Mohammed Al-Saegh, A. Sali, J. S. Mandeep, Alyani Ismail,
Abdulmajeed H.J. Al-Jumaily and Chandima Gomes
Additional information is available at the end of the chapter
http://dx.doi.org/10.5772/58238
. Introduction
As the radio frequency signal radiates through an Earth-sky communication link, its quality
degrades as it propagates through the link because of the absorption and scattering by the
particles in space [ ]. This degradation significantly affects the received information, particu‐
larly with the recent advances in satellite technologies and services, which require a high
information rate. Furthermore, the extent of degradation depends on the link, atmospheric,
transmitted signal, and receiver antenna parameters.
Two types of signal fluctuations caused by atmospheric phenomena, fast and slow fluctuations
[ ], as shown in Figure . The former is called scintillation, which is typically caused by rapid
variations of signal performance attributed to the turbulent refractive index inhomogeneity in
the medium. Meanwhile, slow fluctuations are usually caused by the absorption and scattering
of the signal energy by the particles, particularly water droplets, in the link between the satellite
and the earth station.
With respect to the atmospheric layers, the satellite signal may be subjected to different types
of scintillations. Ionospheric scintillation occurs because of the irregularities in electron density
in the ionosphere [ ] approximately from
km to
km above sea level and, thus,
irregularities in the refractive index. Whereas, tropospheric scintillation is caused by irregu‐
larities in radio refractivity as the wave travels along different medium densities in the
troposphere approximately km to
km above sea level [ ].
The variation of the transmitted signal parameters frequency f and elevation angle θ, in
particular has the major impact on the amount of the atmospheric impairments. For the
transmitted signal frequencies below GHz, the ionospheric scintillation has a significant
© 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
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MATLAB Applications for the Practical Engineer
Figure 1. Fast and slow fluctuations
effect. However, this phenomenon becomes negligible as the frequency increases [ ]. Conse‐
quently, for frequencies above
GHz, other phenomena, such as rain and clouds, impose a
serious impact on the signal attenuation [ ]. The oxygen and water vapor particles in space
have a significant effect at higher signal frequencies [ ].
Transmission at a low-elevation angle during the rain, condensed clouds, water vapor and
Oxygen will increase the effective rain, clouds, water vapor, and Oxygen path of the signal on
the medium, respectively, which in turn causes degradation in the received signal level.
Therefore, the engineers in earth stations try to access the nearest possible satellite in order to
increase the elevation angle, and hence, decrease the effect of atmospheric parameters.
The atmospheric impairments effects on the earth sky communication quality increase the need
for developing prediction models in order to index the atmospheric fade level as well as select
the proper fade mitigation technique FMT .
This chapter proposes a complete model of atmospheric propagation to improve the estimation
and the analysis of atmospheric effects on the signal quality in satellite communications using
actual measured parameters. The model is composed of correlated modules that include
channel modules and quality assessment extended modules.
. Channel model
The general satellite system model contains three main components Earth station s , satel‐
lite s , and the link s between them channel/s . The channel and receiver models have been
built using MATLAB as explained in the following subsections.
The satellite link may suffer from poor signal quality owing to atmospheric impairments.
Raindrops cause significant effect at higher transmission frequencies, particularly above
Atmospheric Propagation Model for Satellite Communications
http://dx.doi.org/10.5772/58238
GHz [ ]. Other atmospheric phenomena, such as clouds, water vapor, and oxygen,
significantly affect signal attenuation, especially at higher transmission frequencies. The
models were implemented in Matlab based on the radiowave sector recommendations from
the International Telecommunication Union ITU which proved to be suitable for satel‐
lite communications.
. . Rain attenuation
Rain droplets absorb and scatter the signal energy and cause its power level to attenuate to a
value depending on the size, amount, and shape of the droplets that the signal passes through
as well as the rain rate [ ]. Rain usually occurs at different heights above sea level depending
on a region on the earth.
Several rain attenuation prediction models have been developed which gained world agree‐
ments, such as Crane [ ], group of researchers from International Telecommunication UnionRadiowave sector ITU-R [ , ], DAH [ ], and SAM [ ]. These models were developed
through many years of monitoring and observations. However, less than % of annual time
usually contains rainy events. ITU-R [ ] used this percentage as a starting point for their rain
attenuation prediction model. To recognize the characteristics of rainy conditions in any area
in the world, a percentage of less than % of the time of the year, which includes the rainfall
that causes a significant amount of attenuation as the signal propagated through, is required
to be taken into consideration.
The rain attenuation model shown in Figure has been built and implemented based on
modified ITU-R prediction model. In Particular, the actual measured rain rate in [ ], rather
than the predicted values by the ITU-R model, has been applied to construct a more accurate
rain estimation model. The model has been implemented using Matlab. The initialization
contains values for earth station position parameters latitude, and height above sea level ,
rain parameters rain rate, rain height, and percentage of exceedance time p , and transmitter
parameters frequency f, elevation angle θ, and polarization angle τ .
The developed program performs two procedures simultaneously. The first procedure starts
with obtaining the frequency-dependent rain attenuation empirical values [ ] before calcu‐
lating the rain specific coefficients k and α through Eq.
and .
k=
a=
k H + k V + k H - k V cos q cos
t
k Ha H + k Va v + k Ha H - k Va v cos q cos
k
t
The rain specific attenuation the rain attenuation per km is then calculated using Eq.
depending on the actual measured rainfall rate at p= . % listed in [ ] rather than the ITU
predicted values in [ ].
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MATLAB Applications for the Practical Engineer
θ
τ
Initialization
Slant path length
calculation
Get the empirical
values
Horizontal reduction
factor calculation
Rain specific
coefficients calculation
Vertical adjustment
factor calculation
Rain specific
attenuation calculation
Effective path length
calculation
Calculate the rain
attenuation at p=0.01%
Rain attenuation
calculation
Figure 2. Rain attenuation model
Check p
Figure 2. Rain attenuation model
g Rain = a R
α
.
k
This value will be used in the
second procedure to identify the effective path length as well as
to predict the overall rain attenuation. The horizontal reduction factor rH for . % of the time
can be calculated using Eq. .
rH =
+ .
PH g R
- .
f
(
- e-
PH
)
where PH is the horizontal projection which depends on the slant path length and the elevation
angle, as imposed by Eq.
PH = LS cosq
The slant path length depends on the vertical height from the earth station to the rain height
as well as on θ, as shown in Eq. .
Atmospheric Propagation Model for Satellite Communications
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ì
ï
ï H R - HS
ï
ïï sin q
LS = í
ï
H R - HS
ï
ï
H R - HS
+ sin q
ï sin q +
ER
îï
for q ³ °
for q < °
where HR and HS are the rain and earth station heights above sea level, respectively and ER is
the earth radius
km . The vertical adjustment factor VF can be calculated at . % of the
time using Eqs.
to .
æ H - HS ö
x = tan - çç R
÷÷
è PH rH ø
ì PH rH
ï
ïï cosq
LR = í
ïH - H
S
ï R
ïî sin q
VF =
é
+ sin q ê
ê
ë
for x > q
for x £ q
æ
ç -e
ç
è
q
-x
ö L g
÷ R R - .
÷ f
ø
ù
ú
ú
û
where x depends on the latitude φ of the earth station. The calculation of the horizontal
reduction and vertical adjustment factors in the ITU-R model is based on . % of the time
exceedance because these factors actually indicate the temporal variability of rain drop
dimension and rain height, respectively [ ]. The effective path length can be obtained using
Eq.
, whereas the total rain attenuation at . % of time A . can be calculated using
Eq.
.
LE = LRVF
A
.
= LEg R
Consequently, the predicted rain attenuation at any percentage of time p can be calculated
using Eqs.
and
.
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MATLAB Applications for the Practical Engineer
ì
ïï
b = í- .
ï
ïî- .
j -
j -
% or j ³
If p ³
+ . - .
Arain = A
.
æ p ö
ç
÷
è . ø
If p < % and j <
sin q
-éë .
°
° and q ³
°
Otherwise
+ .
ln p - .
ln A
.
-b
- p sin q ùû
The signal performance during rain events at different transmission parameters is analyzed
along with received signal strength and error rates assessments.
. . Cloud attenuation
The cloud content of liquid water also causes absorption and scattering of electromagnetic
energy especially for frequencies above
GHz, but with less intensity than that of rain [ ].
Cloud attenuation, in addition to the transmission parameters such as the signal frequency
and the elevation angle θ, depends on the cloud parameters such as average height and
thickness, as well as the total columnar content of liquid water in Kg/m liquid water contents
LWC and temperature.
Several models have been developed to estimate cloud attenuation, such as Salonen & Uppala
[ ], ITU-R [ ], DAH [ ], and Altshuler & Marr [ ]. Salonen & Uppala and ITU-R are
identical in terms of the procedure used to predict the cloud attenuation, as shown in Figure
. The only difference between these two models is in the prediction of the LWC.
Initialization
Principal relaxation
frequency calculation
Secondary relaxation
frequency calculation
Complex dielectric
permittivity calculation
Cloud specific attenuation
calculation
Cloud attenuation
calculation
Check LWC
Check θ
Figure 3. Cloud attenuation model
Figure 3. Cloud attenuation model
Atmospheric Propagation Model for Satellite Communications
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The cloud attenuation estimation model has been implemented based on the Salonen & Uppala
and ITU-R models. The implemented model started with the initialization of the aforemen‐
tioned parameters.
The principal and secondary relaxation frequencies are calculated using Eqs.
respectively.
frpri =
.
(L - ) +
-
frsec =
-
and
,
(L - )
L-
where Λ =
/ T , and T is the temperature in Kelvin. The complex dielectric permittivity of
water contents in the cloud is given by
e'=
e"=
e -e
æ f ö
÷
+ç
ç frpri ÷
è
ø
f (e - e
)
+
é æ
ù
f ö ú
÷
frprp ê + ç
ê ç fr ÷ ú
pri ø
úû
ëê è
e -e
+e
æ f ö
+ çç
÷÷
è frsec ø
+
f (e - e
)
é æ
f ö
frsec ê + çç
÷
ê è frsec ÷ø
ë
ù
ú
ú
û
where ε = . +
. Λ– , whereas ε and ε are equal to . and . , respectively. However,
the cloud specific attenuation coefficient can be calculated using Eq.
.
g clouds =
.
f
é æ +e'ö
e "ê - ç
÷
êë è e " ø
ù
ú
úû
The cloud attenuation at any probability depends on the LWC that can be obtained from
radiosonde or radiometric measurements for the region of interest.
æ LWC ö
Aclouds = g clouds ç
÷
è sin q ø
However, the LWC can be predicted using either Salonen & Uppala [ ] or ITU-R study group
[ ] prediction values. The former implies that LWC is obtained from a proposed map
depending on the temperature and height from the cloud base. The latter implies the use of
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MATLAB Applications for the Practical Engineer
the annual values of LWC exceeded at some specific locations for several percentages of annual
time from digital maps they proposed containing LWC values. The values for other desired
locations on Earth can be derived by interpolation.
. . Water vapor and oxygen attenuations
The signal propagating through the atmosphere undergoes a degradation in signal level owing
to the water vapor and dry air components in the transmission medium [ ]. Water particles
absorb and scatter the wave energy more than oxygen.
Water vapor attenuation depends on the weather parameters such as temperature, water vapor
content, and altitude above sea level. The attenuation increases proportionally once the
temperature and relative humidity RH increase. However, oxygen has the paramount effect
among all other gases because the dry atmosphere contains .
% oxygen, thus resulting in
a significant effect on satellite wave frequencies above GHz [ , ]. The oxygen attenuation
analysis differs from other atmospheric impairments, because its effect on all the regions on
the earth remains constant and independent.
Numerous experiments have been conducted [ , ] using radiosonde for the purpose of
observing and predicting the water content and oxygen attenuation. However, the ITU-R
propagation sector came up with a prediction model [ ] that has gained global agreement.
Figures. a and b shows the water vapor and oxygen attenuation models, respectively. The
models, which have been implemented based on the ITU-R approximate estimation model,
were initialized with related parameters such as the transmitted frequency, relative humidity,
mean temperature, and pressure.
Initialization
Initialization
Water vapor specific
attenuation calculation
Oxygen specific
attenuation calculation
Path length for water vapor
contents calculation
Path length for Oxygen
contents calculation
Water vapor attenuation
calculation
Oxygen attenuation
calculation
(a)
(b)
Figure 4. Water vapor and oxygen attenuation models (a) water vapor, and (b) oxygen
Figure 4. Water vapor and oxygen attenuation models (a) water vapor, and (b) oxygen
Oxygen specific attenuation can be determined for frequencies up to
equations listed in Table .
≤
GHz from the
Atmospheric Propagation Model for Satellite Communications
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Frequency
(GHz)
Equation
O f 2rp 2
54 < f ≤ 60
54 f
1.16 2
10 3
ln 62
ln 64
ln 66
f 64 f 66
f 62 f 66
f 62 f 64
4
8
8
O exp
O f 2rp 2 3.02 10 4 rT 3.5
66 < f ≤ 120
Table 1.
0.62 3
f 60
(62 60 )
2
O 60
60 < f ≤ 62
f 118.75
0.283 rT 3.8
2
0.502 6 1 0.0163 f 7 1.0758 7
f 66
1.4346 4
3.02 10 4
O f 2rp 2rT 3.5
1 1.9 10 5 f 1.5
120 < f ≤
350
ln 54
ln
ln
f 58 f 60 8 58 f 54 f 60 1260 f 54 f 58
24
O exp
62 < f ≤ 66
7.2rT 2.8
f 2 0.34r r 1.6
p T
f ≤ 54
1.15 5
2.91 rp 2rT 1.6
10 3
10 3
2
2 1.6
118.75
2.91
f
r
r
p T
0.283 rT 0.3
Oxygen specific attenuation calculation [21]
Table 1. Oxygen specific attenuation calculation [21]
Where rp and rT are coefficients
3 and temperature rT=
/
ξ ϑ related
δ to pressure rp=Pressure/
T respectively. ξn, ϑn, and δ can be obtained from [ ]. The path length for oxygen content can
be determined using Eq.
.
. ( +z +z +z )
LO =
+ .
rp - .
where
z =
z =
z =
+ .
(f .
+ .
.
rp .
)
.
f
rp
.
é æ
ê
exp ê - ç
ç
ê èç .
ë
(
exp .
- .
+ .
)
exp (
æ
- .
ç
ç - .
è
+
rp
. rp
+ .
)
ù
ú
ú
ú
û
ö
÷
. exp - . rp ÷÷
ø
f-
f+ . ´
.
(
)
a
b
f+ .
-
´
-
f + . ´
f
-
ö
÷
÷
f ø
c
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MATLAB Applications for the
Engineer
Practical
Meanwhile, Eq.
is used to calculate the water vapor specific attenuation in dB/km .
gW =f rT . r éë s + s + s + s + s +s + s + s + s ùû ´
-
Where ρ is ρthe water vapor density in g/m . S ..9 can be obtained from Table .
Par
S1
2
3.981 exp 2.23 1 rT f 22
1
2
f 22.235 9.4212 f 22
Equation
S2
11.1412
S7
S3
0.081 exp 6.44 1 rT
6.2912
S8
S4
S5
Table 2.
2
f 321.226
2
2
9.2212
2
844.61 exp 0.17 1 rT f 557 2
1
2
f 557
f 557
2901 exp 0.41 1 rT f 752
1
2
f 752
f 752
f 1780
2
0.955rp rT 0.68 0.006
η1
2
2
f 1780 2
1
f 1780
S9
25.371 exp 1.09 1 rT
f 380
f 448
8.3328 10 4 2 exp 0.99 1 rT
3.661 exp 1.6 1 rT
f 325.153
Equation
S6
11.961 exp 0.7 1 rT
f 183.31
17.41 exp 1.46 1 rT
Par
0.735rp rT 0.5 0.0353rT 4
η2
Water vapor density calculation [21]
Table 2. Water vapor density calculation [21]
The effective path length may vary with respect to season, latitude, and/or climate change.
However, the ITU-R estimated the effective water vapor path length in the troposphere for f
≤
GHz using Eq.
.
LW = .
where
æ
. sW
+
ç +
ç
( f - . ) + . sW
ç
. sW
ç
+
çç
. ) + . sW ( f è( f -
sW =
(
.
+ exp .
- . rp
. sW
.
)
)
+ . sW
ö
÷
÷
÷
÷
÷÷
ø
Atmospheric Propagation Model for Satellite Communications
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The effective water vapor path length is based on the assumption of an exponential atmosphere
to describe the relation between water vapor density and altitude. At this point, the total gases
attenuation AGases oxygen and water vapor attenuations can be predicted using Eq.
.
AGases =
AO + AW g O LO + g W LW
=
sin q
sin q
. Extended model
The extended model has been added to improve the signal quality assessment in satellite
communication networks for several modulation schemes to propose the optimal FMT.
According to the Friis transmission equation [ , ], the received power in dB is the summa‐
tion of the power transmitted PT, the antenna gains of the transmitter GT, and the receiver GR,
and the subtraction of the losses. The link losses are composed of two types, namely, the free
space loss FSL , and the atmospheric losses. The FSL, which depends on the link distance d
and transmitted frequency f= ×
/ λ, can be calculated using Eq.
FSL =
.
æ pd ö
log ç
÷
è l ø
The atmospheric losses discussed in Section are the second type of link losses. The received
carrier power-to-noise ratio is estimated to identify the total degradation of the power in dB.
The total noise depends on the bandwidth, in addition to the system and antenna noise
temperatures. Based on the Friis transmission equation and to analyze the communication
signal quality, the bit energy-to-noise ratio Eb/No can be calculated using Eq.
.
Eb N o dB = EIRP + Gr - FSL - LA - LS - N o -
log ( Rb )
where EIRP is the Effective Isotropic Radiated Power and LA, Ls, No and Rb are the atmospheric
losses, system losses, noise spectral density, and bit rate, respectively. The atmospheric
impairments negatively affect the data after being demodulated in the receiver. The effects
appear as a decrease in Eb/No and bit error rate BER . These two metrics are used in selecting
the optimal FMT at the time instants. The BER caused by atmospheric impairments can be
approximated based on a Gray-code using Eq. [ ].
BER »
SER
log ( M )
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MATLAB Applications for the Practical Engineer
where the symbol error rate SER is calculated using the equations listed in [ ] for three
modulation schemes QPSK, -PSK and -PSK. The extended model has been added in order
to improve the signal quality assessment in satellite communication networks for several
modulation schemes to propose the optimal FMT. The extension includes several steps in the
receiver side of the system, and comprises the calculation of the received signal power with
the extraction of the atmospheric effects, Eb/No that indicates the signal quality, and BER.
. Indexing and FMT
Recent satellite communications technologies make massive use of resource management
procedures such as channel state reporting and FMT. The procedure for channel state reporting
indexing is a fundamental feature of satellite networks utilizing FMT since it enlists the
estimated channel quality level at the receiver, and then send this report to the transmitter to
apply specific technique in the next period of time. Each index is calculated as a quantized and
scaled measure of the experienced Eb/No, Es/No, BER or SER. The reported values can be used
to make decisions concerning the resource allocation to users experiencing specific channel
conditions or application of a certain FMT. In TDM/TDMA transmitters, the decision is applied
for a time period called transmission time interval TTI . For every TTI, the allocation decision
or FMT is performed with a validation until the next TTI.
FMT is usually classified into two types [ ]. The first type mainly deals with a variation in
signal characteristics such as Adaptive coding and modulation ACM, time diversity, or
frequency diversity . The second type does not concern the signal modifications such as power
control, or site diversity .
The reporting procedure is related to the FMT module, which selects the proper modulation
and coding scheme in a case where a satellite network use the ACM technique for the process
of maximizing the supported throughput with a given target error rate. Therefore, a user
experiencing higher Eb/No will be served with higher bitrates, whereas a user experiencing poor
channel conditions, will be served with lower bitrates to maintain active connections with
lower error bits. It is worth to mention that the number of modulation and coding schemes is
limited. Therefore, the throughput is upper-bounded over a specific threshold and the increase
in the Eb/No does not result in any gain in throughput. This is the main reason of applying ACM
technique accompanied with the power control technique.
The power control technique uplink or downlink power control is a dynamic procedure that
adjusts transmission power to compensate for instantaneous channel condition variations [ ].
These adjustments reduce power while maintaining a constant bitrate, or boost power to
decrease losses when a higher modulation and coding scheme are selected, thus, increasing
the bitrate. Henceforth, the aim is to keep the expected error rate below a target threshold.
However, some satellite networks consist of two or more ground stations spatially separated
by at least
km [ ] to provide separate propagation paths to the signal. This technique is
called site diversity. The idea is to provide two different satellite channels that will not be
significantly affected by rain attenuation simultaneously. This process enables the use of the
best channel condition with a higher received signal level.
Atmospheric Propagation Model for Satellite Communications
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The transmitted signal can also be repeated at different time frames. Therefore, the receiver
will receive more than one copy of the data transmitted. This technique is called time diversity.
The time separation between successive repetitions should be greater than the channel
coherence time to prevent the correlation of the received signals. Finally, frequency diversity
involves transmitting the same message simultaneously at sufficiently separated more than
the coherence bandwidth transmitted frequencies.
. Complete proposed propagation model
The complete proposed propagation model for the satellite network is shown in Figure . The
model consists of three parts the transmitter, channel, and receiver. The modules in the
channel and the receiver are the main concern of this chapter.
Transmitter
PDU
Transmission parameters
Modulation
Mitigation technique
Free Space Loss
Oxygen attenuation model
Channel
Water vapor attenuation
Cloud attenuation model
Rain and site parameters
Rain attenuation model
Yes
Check Rain
No
Atm. impairments
Received power
Extraction of Atm. effects
Receiver
Signal quality indication
Error rates calculation
Indexing
Figure 5. Complete propagation model
The packet data units are transmitted using specific transmission parameters and mitiga‐
tion technique selected through the reported satellite channel. The effect of FSL, dry air
oxygen , and water vapor attenuation are added before the cloud attenuation module. The
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MATLAB Applications for the Practical Engineer
availability of rain attenuation is then checked. The rain and position parameters are also
identified for the purpose of determining the effect of rain to the signal power. The total
atmospheric impairments are then calculated to evaluate the received signal power as
mentioned in section .
The atmospheric impairments and their effects on the received signal signified by Eb/No are
then evaluated at different elevation angles. Eb/No is used for the signal quality indication and
BER evaluation at different modulation schemes. The obtained values are then indexed in the
process of estimating the instantaneous satellite channel quality. These values are then
reported to the transmitter for the selection of the appropriate FMT to the next TTI. The model
has been applied and the results were obtained at specific modules with signal evaluation and
assessment under different atmospheric and transmission parameters.
. Results and discussion
The frequency of the satellite signal transmitted during rain events has a significant effect on
the amount of signal power attenuation as shown in Figure . The analysis involved a satellite
terminal located in Selangor, Malaysia Latitude . N, Longitude
. E , and the θ was
fixed to º. It is clearly shown that the GHz C-band transmitted frequency has very low
amount of attenuation even at heavy rain events . % of time .
Figure 6. Rain attenuation at different percentages of time and frequencies
At . GHz Ku-band frequency, the rain attenuation at . % of time A . is approximately
. dB and reached approximately . dB at .
% of the time, whereas A . reached
approximately
dB if the signal was transmitted at
GHz Ka-band carrier frequency. The
elevation angle is a highly effective parameter for the signal quality degradation. Figure
shows the amount of rain attenuation at different frequencies and elevation angles.
Atmospheric Propagation Model for Satellite Communications
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Figure 7. Rain attenuation at different θ and f
During rain events, the transmission link elevation angle is inversely proportional to the
effective rain path of the signal and hence the amount of rain attenuation, whereas the
transmitted frequency is directly proportional to the rain attenuation value. Consequently, the
satellite position in space and the earth station terminal identifies the θ of the communication
link. With the aid of the results in Figure , the rain attenuation at three different satellites
connected to earth station in Selangor has been analyzed as shown in results listed in table
for several frequencies.
Satellite
MEASAT 3/3A
(91.5º E)
SUPERBIRD C2
(144º E)
INTLESAT 19
(166º E)
Elevation
Rain attenuation (dB)
1.5GHz
3.5 GHz
6 GHz
12 GHz
22 GHz
L-Band
S-Band
C-Band
Ku-Band
Ka-Band
77.5º
0.01
0.2
2.36
17.6
62.5
41.1º
0.02
0.29
3.73
27.3
88.7
17.4º
0.05
0.67
9.93
67.8
191
angle (θ)
Table 3. Rain attenuation at different satellites and transmission frequencies
It’s obvious that there is a difference in the amount of rain attenuation at the same frequency
but with different satellite different θ . Consequently, there is a significant change in rain
attenuation as the frequency band changed for the same satellite fixed θ .
For cloud attenuation, the transmitted frequency and amount of liquid water in the cloud have
a major effect on signal power attenuation. Figure displays these effects on the amount of
cloud attenuation.
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MATLAB Applications for the Practical Engineer
(a)
(b)
Figure 9. Gases attenuation.
θ
θ
Figure 8. Cloud attenuation at different f, θ, and LWC
Figure shows that the effect of clouds at Ku band frequencies is almost negligible for all θ
and LWC, whereas the cloud attenuation at
GHz Ka frequency band are below dB for a
different LWC at θ > º, and below dB for lower θ at and Kg/m LWC. At
GHz V
frequency band, a significant amount of cloud attenuation exceeding
dB for LWC=
Kg/m and θ below º was observed. Whereas it reached . dB, . dB, and . dB for θ= . º
and LWC was , , and Kg/m , respectively. Consequently, cloud attenuation reached
approximately . dB, . dB, and . dB if the if θ= . º and LWC was , , and Kg/m ,
respectively.
The significant amount of dry air and water vapor specific attenuation appears at specific
regions across the frequency spectrum, and hence the total correlated gases attenuation, as
shown in Figure a
Atmospheric Propagation Model for Satellite Communications
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The significant specific attenuation started at frequencies above
GHz mainly due to the
effect of oxygen, and then the attenuation level went down. The effect appeared again at
frequencies above
GHz, but this time mainly due to water vapor attenuation. The gases
attenuation at fixed % RH reached higher level at approximately
GHz. The relative
humidity RH is directly proportional to the amount of signal power attenuation due to the
water vapor particles in space, and hence the total gases attenuation as shown in Figure b.
However, the regions near the sea and the equator usually suffer from higher RH which
indicates increased gases attenuation.
The channel quality level can be identified by the value of Eb/No. This leads to the selection of
the proper FMT for the next TTI. The channel quality during atmospheric impairments is
varied according to several parameters. θ is one of the major parameters that specify the
channel quality during atmospheric dynamics. Figure shows the Eb/No with various rainfall
events at different θ for . GHz transmitted frequency.
Figure 10. Bit energy to noise ratio for different θ
The higher the elevation angle, the lower the attenuation and therefore the higher the value of
Eb/No. During very heavy rain around .
% of annual time , bad channel quality imposes
serious problems to the users of the satellite networks. This leads to communication link outage
at lower θ. The BER calculations depends on the Eb/No along with the transmission bit rate and
bandwidth. Figure
shows the BER approximated for three modulation schemes QPSK, PSK, and -PSK.
As the number of bits per second increased with the M-ary modulation scheme, the number
of error bits increased simultaneously during the signal propagation through the atmosphere.
For rainy weather events, the higher the M-ary modulation scheme, the higher the BER due to
M-ary is a term derived from the word binary. M represents a digit that corresponds to the modulation order. M= , ,
and
for the QPSK, -PSK, and -PSK modulation schemes, respectively. more details in [ ].
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MATLAB Applications for the Practical Engineer
Figure 11. Bit error rate
the higher number of transmitted bits in unit time. Consequently, Figure also clarifies that
the θ is inversely proportion to BER. QPSK modulation can be considered the most appropriate
modulation scheme in case of high atmospheric impairments, but at the cost of a lower number
of transmitted bits per unit time.
. Conclusion
This chapter presented the atmospheric impairments to the satellite signal quality in terms
of performance evaluation and assessments concerning various effective atmospheric and
transmission parameters during dynamic weather conditions. The impairments presented
were caused by rain, clouds, dry air oxygen , and water vapor attenuation. An overview
of ionospheric and tropospheric scintillations, channel status reporting indexing , and FMT
was provided. The atmospheric propagation model was introduced. The model included a
transmitter, channel, and receiver modules built using Matlab which was revealed to be
appropriate for building mathematical and analytical models. Channel conditions were
evaluated along with quality and error rate estimation extensions. The atmospheric
impairments results were obtained based on actual measured real-world parameters. The
performance analysis of the proposed extended and propagation modules for the satellite
system included atmospheric attenuation, signal-to-noise ratios, bit energy-to-noise ratios,
as well as BER. The results showed that the rain attenuation effect started at frequencies
above
GHz and exhibited the largest effects among other atmospheric phenomena,
followed by cloud attenuation, and gases attenuation that have the least effects. More‐
over, the results revealed that the transmitted frequency, rainfall rates, LWC, and relative
humidity are directly proportional to the signal quality degradation, whereas θ is inverse‐
ly proportional. The reported channel quality, which indicated by Eb/No, under poor
Atmospheric Propagation Model for Satellite Communications
http://dx.doi.org/10.5772/58238
conditions may suffer from link outage at heavy rain events for low θ. This condition
corresponds to low BER, particularly at a higher M-ary modulation scheme. The chapter
would be useful for satellite system designer to accurately predict the atmospheric
impairments that may affect the channel, and identifying signal quality performance with
error rates during weather dynamics.
Appendix: Matlab code
%----------------------------------------------------------------------------------------% Initialization
%----------------------------------------------------------------------------------------% Input parameters:
% The values of these parameters can be adjusted according to the scenario or region of interest.
% Transmission parameters
disp('Transmission parameters:');
command = 'Input the elevation angle (Degree): ';
Elev = input(command);
if Elev<5 || Elev>90
error('please input value from 5 to 90 ')
end
command = 'Input the transmission frequency (GHz): ';
f=input(command);
if f<0.1 || f>350
error('please input value from 0.1 to 350 ')
end
command = 'Input the polarization? (1 for Horizontal, 2 for Vertical, and 3 for Circular): ';
pol= input(command);
if pol==1
tau=0;
elseif pol==2
tau=90;
elseif pol==3
tau=45;
else
error('please input integers from 1 to 3 ')
end
disp('=======================');
% Atmospheric parameters
disp('Atmospheric parameters:');
command = 'Input percentage of exceedance time (from 0.001% to 5%): ';
P = input(command);
command = 'Input Rain Rate for 0.01% of time (mm/h): ';
Rrate = input(command);
% Some actual measured rain rate values can be obtained from [6].
command = 'Input Rain height above sea level (km): ';
hR = input(command);
command = 'Input liquid water contents LWC (Kg/m^2): ';
LWC = input(command);
command = 'Input Temperature (K): ';
T = input(command);
command = 'Input the relative humidity (%): ';
RH = input(command);
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MATLAB Applications for the Practical Engineer
if RH<0 || RH>100
error('please input value from 0 to 100 ')
end
command = 'Input pressure (hPa): ';
p = input(command);
disp('=======================');
% Satellite and earth station parameters
disp(Satellite and earth station parameters:');
command = 'Input the Effective Isotropic radiated power (dBW): ';
EIRP = input(command);
command = 'Input earth station Latitude : ';
Lat = input(command);
command = 'Input Station height above sea level (km): ';
hs = input(command);
command = 'Input receiver gain (dBi): ';
gr = input(command);
disp('=======================');
%----------------------------------------------------------------------------------------% Rain attenuation
%----------------------------------------------------------------------------------------% Frequency-dependent rain attenuation empirical values (K and alpha)
% In order not to take much space, the empirical values are defined here for
% frequency range from 11 to 14 GHz
% other frequency values can be obtained from ITU-R P.838 recommendation.
% Source: http://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.838-3-200503-I!!PDF-E.pdf
if f==11
kH=0.01772;
kV=0.01731;
alphaH=1.2140;
alphaV=1.1617;
elseif f<12 && f>11
kH=(((f-11)/(12-11))*(0.02386-0.01772))+0.01772;
kV=(((f-11)/(12-11))*(0.02455-0.01731))+0.01731;
alphaH=(((f-11)/(12-11))*(1.1825-1.2140))+1.2140;
alphaV=(((f-11)/(12-11))*(1.1216-1.1617))+1.1617;
elseif f==12
kH=0.02386;
kV=0.02455;
alphaH=1.1825;
alphaV=1.1216;
elseif f<13 && f>12
kH=(((f-12)/(13-12))*(0.03041-0.02386))+0.02386;
kV=(((f-12)/(13-12))*(0.03266-0.02455))+0.02455;
alphaH=(((f-12)/(13-12))*(1.1586-1.1825))+1.1825;
alphaV=(((f-12)/(13-12))*(1.0901-1.1216))+1.1216;
elseif f==13
Atmospheric Propagation Model for Satellite Communications
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kH=0.03041;
kV=0.03266;
alphaH=1.1586;
alphaV=1.0901;
elseif f<14 && f>13
kH=(((f-13)/(14-13))*(0.03738-0.03041))+0.03041;
kV=(((f-13)/(14-13))*(0.04126-0.03266))+0.03266;
alphaH=(((f-13)/(14-13))*(1.1396-1.1586))+1.1586;
alphaV=(((f-13)/(14-13))*(1.0646-1.0901))+1.0901;
elseif f==14
kH=0.03738;
kV=0.04126;
alphaH=1.1396;
alphaV=1.0646;
% Update the frequency-dependent rain attenuation empirical values here
end
k=(kH+kV+((kH-kV)*((cosd(Elev)).^2)*cosd(2*tau)))/2;
alpha=((kH*alphaH)+(kV*alphaV)+(((kH*alphaH)(kV*alphaV))*((cosd(Elev)).^2)*cosd(2*tau)))/(2*k);
gamaR=k*(Rrate.^alpha);
% The rain specific attenuation (dB/km)
disp(['Rain specific attenuation= ', num2str(gamaR),' dB/km']);
if Elev >=5
% slant-path length below the rain height (km)
Ls=(hR-hs)/(sind(Elev));
end
LG=Ls.*cosd(Elev);
% The horizontal projection of the slant path length (km).
% Calculation of the horizontal reduction factor for 0.01% of the time
c=0.78.*sqrt(LG.*gamaR/f);
d=0.38.*(1-exp(-2.*LG));
ro_o1=1/(1+c-d);
% Calculation of the vertical adjustment factor for 0.01% of the time
eta=atand((hR-hs)/LG.*ro_o1);
if eta >Elev
LR=(LG.*ro_o1)/cosd(Elev);
else
LR=(hR-hs)/sind(Elev);
end;
abslat=abs(Lat);
if abslat<36
kye=36-abslat;
else
kye=0;
end;
e=31.*(1-exp(-Elev/(1+kye)));
fff=sqrt(LR.*gamaR)/f.^2;
vo_o1=1/(1+sqrt(sind(Elev)).*(e.*fff-0.45));
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MATLAB Applications for the Practical Engineer
% Effective path length (km)
LE=LR*vo_o1;
% Rain attenuation at P=0.01% of time (dB)
RAtt01=LE*gamaR;
% Calculation of rain attenuation at different percentages of time
if P >=(1)||(abs(Lat)>=36)
beta=0;
elseif P <(1)&& (abs(Lat)<36)&&(Elev>=25)
beta=-0.005.*(abs(Lat)-36);
else
beta=-0.005.*(abs(Lat)-36)+1.8-4.25.*sind(Elev);
end
if P >=(0.001) && (P <=5)
pu=(-1).*(0.655+(0.033.*log(P))-(0.045.*log(RAtt01))-(beta.*(1-P).*sind(Elev)));
RainAttP=RAtt01.*(P/0.01).^(pu);
else error('Percentage of exceedance time should be from 0.001% to 5% for...
predicting the rain attenuation' )
end
disp(['Rain attenuation= ', num2str(RainAttP),' dB']);
%----------------------------------------------------------------------------------------% Cloud attenuation
%----------------------------------------------------------------------------------------% Calculation of the principal & secondary relaxation frequencies:
theta=300/T;
eo=77.6+103.3.*(theta-1);
e1=5.48;
e2=3.51;
fp=20.09-142.*(theta-1)+294.*(theta-1)^2; %GHz
%GHz
fs=590-1500.*(theta-1);
% Calculation of the complex dielectric permittivity of water:
eta2=(f/fp).*((eo-e1)/(1+(f/fp)^ 2))+(f/fs).*((e1-e2)/(1+(f/fs)^2));
eta1=((eo-e1)/(1+(f/fp)^2))+((e1-e2)/(1+(f/fs)^2))+e2;
n=(2+eta1)/eta2;
% cloud specific attenuation coefficient ((dB/km)/(g/m^3))
Kl=0.819.*f/(eta2.*(1+n^2));
CAtten=LWC.*Kl/sind(Elev);
disp(['Cloud attenuation= ', num2str(CAtten),' dB']);
Atmospheric Propagation Model for Satellite Communications
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%----------------------------------------------------------------------------------------% Gases attenuation
%----------------------------------------------------------------------------------------% (1) Water vapor
% calculation of the water vapor specific attenuation
th=300/T;
pw=(RH/5.752)*th*(10.^(10-(9.834*th))); % Water vapor density (g/m^3)
rt=288/T;
rp=p/1013;
n1=0.955*rp*(rt.^0.68)+(0.006*pw);
n2=0.735*rp*(rt.^0.5)+(0.0353*(rt.^4)*pw);
Yw=(((3.98*n1*exp(2.23*(1-rt)))/(((f-22.235).^2)+9.42*(n1.^2)))*(1+((f-22)/(f+22)).^2)+...
((11.96*n1*exp(0.7*(1-rt)))./(((f-183.31).^2)+11.14*(n1.^2)))+((0.081*n1*exp(6.44*(1-rt)))/...
(((f-321.226).^2)+(6.29*(n1.^2))))+((3.66*n1*exp(1.6*(1-rt)))/(((f-325.153).^2)+9.22*(n1.^2)))+...
((25.37*n1*exp(1.09*(1-rt)))/((f-380).^2))+((17.4*n1*exp(1.46*(1-rt)))/((f-448).^2))+...
((844.6*n1*exp(0.17*(1-rt)))/((f-557).^2))*(1+((f-557)/(f+557)).^2)+((290*n1*exp(0.41*(1-t)))/...
((f-752).^2))* (1+((f-752)/(f+752)).^2)+ ((83328*n2*exp(0.99*(1-rt)))/...
((f-1780).^2))* (1+((f-1780)/(f+1780)).^2))*((f.^2)*(rt.^2.5)*(pw*10.^(1-5)));
disp(['water vapor specific attenuation= ', num2str(Yw),' dB/km']);
% calculation of the path length for water vapor contents (km)
conw=1.013/(1+exp((0-8.1)*(rp-0.57)));
hw=1.66*(1+((1.39*conw)/(((f-22.235).^2)+(2.56*conw)))+((3.37*conw)/(((f-183.31).^2)+...
(4.69*conw)))+((1.5*conw)/(((f-325.1).^2)+(2.89*conw))));
Aw=Yw*hw; %water vapor attenuation in zenith angle path (dB)
% (2) Dry air
% Definitions:
ee1=(rp.^0.0717)*(rt.^(0-1.8132))*exp(0.0156*(1-rp)-1.6515*(1-rt));
ee2=(rp.^0.5146)*(rt.^(0-4.6368))*exp((0-0.1921)*(1-rp)-5.7416*(1-rt));
ee3=(rp.^0.3414)*(rt.^(0-6.5851))*exp(0.2130*(1-rp)-8.5854*(1-rt));
ee4=(rp.^(0-0.0112))*(rt.^(0.0092))*exp((0-0.1033)*(1-rp)-0.0009*(1-rt));
ee5=(rp.^0.2705)*(rt.^(0-2.7192))*exp((0-0.3016)*(1-rp)-4.1033*(1-rt));
ee6=(rp.^0.2445)*(rt.^(0-5.9191))*exp(0.0422*(1-rp)-8.0719*(1-rt));
ee7=(rp.^(0-0.1833))*(rt.^(6.5589))*exp((0-0.2402)*(1-rp)+6.131*(1-rt));
Y54=2.192*(rp.^1.8286)*(rt.^(0-1.9487))*exp(0.4051*(1-rp)-2.8509*(1-rt));
Y58=12.59*(rp.^1.0045)*(rt.^(3.5610))*exp(0.1588*(1-rp)+1.2834*(1-rt));
Y60=15*(rp.^0.9003)*(rt.^(4.1335))*exp(0.0427*(1-rp)+1.6088*(1-rt));
Y62=14.28*(rp.^0.9886)*(rt.^(3.4176))*exp(0.1827*(1-rp)+1.3429*(1-rt));
Y64=6.819*(rp.^1.4320)*(rt.^(0.6258))*exp(0.3177*(1-rp)-0.5914*(1-rt));
Y66=1.908*(rp.^2.0717)*(rt.^(0-4.1404))*exp(0.4910*(1-rp)-4.8718*(1-rt));
ss=(0-0.00306)*(rp.^3.211)*(rt.^(0-14.94))*exp(1.583*(1-rp)-16.37*(1-rt));
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MATLAB Applications for the Practical Engineer
% Calculation of the dry air specific attenuation
if f<=54
Yo=(((7.2*(rt.^2.8))/((f.^2)+(0.34*(rp.^2)*(rt.^1.6))))+((0.62*ee3)/(((54-...
f).^(1.16*ee1))+(0.83*ee2))))*((f.^2)*(rp.^2)*(10.^(0-3)));
elseif f>54 && f<=60
Yo=exp(((log(Y54)./24)*(f-58)*(f-60))-((log(Y58)./8)*...
(f-54)*(f-60))+((log(Y60)./12)*(f-54)*(f-58)));
elseif f>60 && f<=62
Yo=Y60+((Y62-Y60)*((f-60)/2));
elseif f>62 && f<=66
Yo=exp(((log(Y62)./8)*(f-64)*(f-66))-((log(Y64)./4)*(f-62)*...
(f-66))+((log(Y66)./8)*(f-62)*(f-64)));
elseif f>66 && f<=120
Yo=((3.02*(10.^(0-4))*(rt.^3.5))+((0.283*(rt.^3.8))/(((f-...
118.75).^2)+(2.91*(rp.^2)*(rt.^1.6))))+...
((0.502*ee6*(1-(0.0163*ee7*(f-66))))/(((f-66).^(1.4346*ee4))+...
(1.15*ee5))))*((f.^2)*(rp.^2)*(10.^(1-4)));
elseif f>120 && f<=350
Yo=(((3.02*(10.^(0-4)))/(1+(1.9*(10.^(0-5))*(f.^1.5))))+...
((0.283*(rt.^0.3))/(((f-118.75).^(2))+(2.91*(rp.^2)*...
(rt.^1.6)))))*((f.^2)*(rp.^2)*(rt.^3.5)*(10.^(0-3)))+ss;
end
disp(['Dry air specific attenuation= ', num2str(Yo),' dB/km']);
% Calculation of the equivalent height
t1=(4.64/(1+(0.066*(rp.^(0-2.3)))))*exp(0-((f-59.7)/(2.87+(12.4*exp((0-7.9)*rp)))).^2);
t2=(0.14*exp(2.12*rp))/(((f-118.75).^2)+0.031*exp(2.2*rp));
t3=(0.0114/(1+(0.14*(rp.^(0-2.6)))))*f*((0-0.0247+(0.0001*f)+...
(1.61*(10.^(0-6))*f.^2))/(1-(0.0169*f)+(4.1*(10.^(0-5))*f.^2)+ (3.2*(10.^(0-7))*f.^3)));
ho=(6.1/(1+(0.17*(rp.^(0-1.1)))))*(1+t1+t2+t3);
Ao=Yo*ho;
% Dry air attenuation in zenith angle path (dB)
% Total gases attenuation
Ytot=(Yo+Yw)./sin(Elev);
disp(['Total gases specific attenuation= ', num2str(Ytot),' dB/km']);
Atot=(Ao+Aw)./sin(Elev);
disp('=======================');
%----------------------------------------------------------------------------------------% Extended model
%----------------------------------------------------------------------------------------disp('Extended model');
disp('============');
disp('Other Input parameters:');
command = 'Input the bit rate (kb/s): ';
brate = input(command)*1000;
Atmospheric Propagation Model for Satellite Communications
http://dx.doi.org/10.5772/58238
command = 'Input the noise spectral density: ';
No = input(command);
command = 'Input total system losses (dB) : ';
Ls = input(command);
% Calculating the free space loss (FSL)
lambda=(3*1e8)/(f*10^9);
fsl= 20*log10((4*pi*3.6*1e7)./lambda);
disp(['Free space Loss = ', num2str(fsl),' dB']);
command = 'Input the atmospheric loss (dB) : ';
La = input(command);
% Here you can define the atmospheric loss (La) according to the specifiedscenario,
% or you can use the outputs from the aforementioned models.
% Error rate mathematical model can be added as mentioned in section 3.
% Quality indication: Calculating the bit energy to noise ratio
Rpower=EIRP+gr-fsl-La-Ls;
% received power (dBW)
ebnor=Rpower-No-10*log10(brate);
disp(['Eb/No = ', num2str(ebnor),' dB']);
%-----------------------------------------------------------------------------------------
Acknowledgements
Ministry of Higher Education MoHE in Malaysia is thankfully acknowledged for the grant
with code ERGS/ /
.
Author details
Ali Mohammed Al-Saegh , A. Sali , J. S. Mandeep , Alyani Ismail ,
Abdulmajeed H.J. Al-Jumaily and Chandima Gomes
Department of Computer and Communication Systems Engineering, Universiti Putra
Malaysia, Serdang, Selangor, Malaysia
Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan
Malaysia, Bangi, Selangor, Malaysia
Department of Electrical and Electronic Engineering, Universiti Putra Malaysia, Serdang,
Selangor, Malaysia
273
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