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Project Report on
“Studies of Fade mitigation control for microwave satellite signal propagation”
G.S Sanyal School of Telecommunications Indian Institute of Technology Kharagpur, Kharagpur 721 302, West Bengal, India.
Principal Investigators:
1. Dr. Suvra Sekhar Das
Assistant Professor
GSSST, IIT Kgaragpur
2. Prof. Kalyan Kumar Baandopadyay
Professor
Department of E & ECE, IIT Kharagpur
Team Members:
1. Jayeeta Saha
M.S first year
10GS7001
2. Srinivas Sagar
M.Tech final year
08EC6414
3. Santanu Mondal
B.Tech final year
06EC1013
2
Contents
1 Introduction 6
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 State of Art in satellite communication . . . . . . . . . . . . . . . . . . . . 7
1.4 Problem area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Fade Mitigation Techniques(FMT) 9
2.1 Link adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 BER performance for different modulation schemes . . . . . . . . 9
2.2 Propagation effects and their impact on satellite-earth links . . . . . . . . 11
2.3 Fade Mitigation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.1 Power control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.2 Adaptive waveform . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.3 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.4 Layer 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Implementation of FMT 18
3.1 FMT control logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 Implementation of FMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 Description of simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.1 CRC Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.2 Error Control Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.3 Modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3.4 Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3.5 Automatic Gain Control (AGC) . . . . . . . . . . . . . . . . . . . . 21
3.3.6 demodulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.7 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3.8 CRC Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Channel 23
3
5 Detection 25
5.1 Measurments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.1.1 Open-loop Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.1.2 Closed-loop Detection . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.1.3 Hybrid-loop Detection . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.2 Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
5.3 Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.4 Adaptive system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6 Decision 28
6.1 Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.1.1 Detection margin and Hysteresis . . . . . . . . . . . . . . . . . . . 29
6.2 Decision making algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.2.1 fade control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
7 Results 33
7.1 BER performance for different modulation schemes . . . . . . . . . . . . 33
7.1.1 Change of modulation scheme and coding rate with respect to
time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
7.2 BER performance for the collected data . . . . . . . . . . . . . . . . . . . 34
7.2.1 PER performance for different SNR . . . . . . . . . . . . . . . . . 35
7.2.2 Performance of the FMT simulator with time . . . . . . . . . . . . 35
8 Practical Implementation with Modem (SRM6100) 39
8.1 About the Modem-SRM6100 . . . . . . . . . . . . . . . . . . . . . . . . . . 39
8.2 Experiments to be done with the modem SRM6100 . . . . . . . . . . . . . 39
8.3 Experiments done with the modem SRM6100 . . . . . . . . . . . . . . . . 39
9 Plan of the experiment 41
9.1 Objective of the experiment . . . . . . . . . . . . . . . . . . . . . . . . . . 41
9.2 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
9.3 Experimental Set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
9.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
9.5 Resources required . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
9.6 Expected Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
10 Plan and Budget of project 44
10.1 Plan of Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
10.2 Budget of Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
11 Conclusion 46
4
Bibliography 48
5
Chapter 1
Introduction
1.1 Background
In Telecommunication the use of satellite is to provide communication links between
various points on earth. Communication satellites relay voice,video, and data signals
between widely separated fixed location. The technique involves transmitting signals
from an earth station to a satellite, the satellite will receive the signals, amplifies them,
and retransmits them to a region of earth. Receiving stations within this region pick
up the signals thus completing the link. Satellite systems operate in the microwave
and millimeter wave frequency bands, using frequencies between 1 and 50 GHz.
All radio systems require spectrum, and the delivery of high speed data requires a
wide bandwidth. Satellite communication systems started in C band, with ana allo-
cation of 500 MHz, shared with terrestrial links .As the GEO orbit is filled up with
satellites operating at C band, satellites were built for the next available frequency
band, Ku band. There is a continuing demand for ever more spectrum to allow satel-
lite to provide new services, with high speed access to the internet forcing a move to
the Ka band and even higher frequencies.
Successive world radio conferences have allocated new frequency bands for commer-
cial satellite services that now include L,S,C,Ku,Ka,V ,and Q bands. Mobile satellite
systems use VHF, UHF,L,and S bands with carrier frequencies from 137 to 2500 MHz,
and GEO satellites use frequency bands extending from 3.2 to 50 GHz. Despite the
growth of fiber optic links with very high capacity, the demand for satellite system
continues to increase.
1.2 Motivation
There is worldwide interest, including ISRO, to use higher than C band spectrum in
future Satellite Communication Systems. They offer several advantages for Satellite
6
Communications over C band like, spectrum availability, reduced terrestrial Interfer-
ence potential and reduced equipment size.Use of static margins takes away a huge
amount from the link budget leaving the options of low data rates.however if we
use systems such as link adaptation systems where link level parameters are dynam-
ically adjusted in order to maximize the data rates over a certain period of time. Sev-
eral results exists for terrestrial cellular communication systems, but these have not
much been experimented for Satellite Communication systems and especially above
C band.So it will be very interesting to implement the link adaptation at higher fre-
quency bands in satellite communication.
1.3 State of Art in satellite communication
Although Ka and Q/V bands are attractive from the point of view of the amount of
frequency bandwidth that the satellite can potentially use, some important limitation
could moderate the enthusiasm of using them if specific techniques were not imple-
mented in the satellite system to guarantee the capacity, the availability and the qual-
ity of service. The major limitation is the effect of radio-wave propagation through
the lowest layers of the atmosphere. As the operating frequency is increased, the at-
tenuation and scintillation effects of atmospheric gas, clouds and rain become more
severe, the direct consequence is the need to implement high system static margins, in
order to insure a minimum outage duration of the service, for a given objective of link
availability.
All kinds of propagation impairments have to be taken into consideration and details
regarding these attenuations are presented in [6].Once we come to know the amount of
attenuation in the atmosphere we will calculate the link budget. Link budget is noth-
ing but the tabular method of calculating the received power and noise power. When
we calculate our link budget then we come to know how much amount of margin we
have and accordingly we will go for implementing the fade mitigation techniques if
necessary.The different kinds of fade mitigation techniques are presented in the ref-
erences [1] and [3].Whatever Fade Mitigation Technique used, a control loop will be
necessary in order to track the propagation channel variations and compensate the
impairments, as accurately as possible, with the activation of the FMTwhen (and only
when) it is needed.The details of this FMT control logic is presented in reference [2].
and basic concepts of this satellite communication have been read from the reference
[4].
The FMT simulator that is being designed for GSAT-IV.Now-a-days ,the demand for
more bandwidth that can be met by allocating higher spectrum. The bandwidth re-
quirement for Ka-band is 1.5 GHz .The uplink and downlink frequency range is 30-20
7
GHz..
1.4 Problem area
The constant growth of communication services, both in number of users and amount
of data rate, and the limited available frequency resources at Ku-Band, pushes the
satellite industry to consider implementation of future satellite systems operating at
Ka-band and above where large bandwidths are available. As the operating frequency
is increased, the attenuation effects of atmospheric gas, clouds and rain and scintilla-
tion become more important.As it is not cost efficient to design a large power margin,
link signal fading must be compensated by other means in order to increase system
availability. Using Fade Mitigation Techniques a system can adapt its physical layer to
the propagation channel variations, optimizing system capacity in clear sky and reach-
ing the required availability during unfavorable propagation conditions. The success
of FMT systems will depend on both the technique used to counteract the fade and the
control logic algorithm in charge of the real time tracking of the propagation channel.
Fade Mitigation Technique have to be considered and have to be introduced into the
system through the design of a control loop, which aims at mitigating a propagation
event in real time, by adapting some systems parameters : transmitted power, coding,
modulation. The dynamics of the channel is therefore a key element to be taken into
account directly into the definition of FMT control loop.
The implementation of Fade Mitigation Technique (FMT) leads to a specific design of
adaptive systems, with several functions to be implemented : detection of the fade
level, prediction of the attenuation a short time ahead, decision making and activation
of a FMT mode. FMTs are introduced through the design of a control loop, which
should track the signal variations, especially the slow component (attenuation), and
possibly the envelope of fast fluctuations.
Due to the congestion of the radioelectric spectrum and in order to offer broader trans-
mission channels for multimedia applications, satellite communication systems are
evolving toward higher frequency bands. An increasing number of new services are
being promoted for Ka-band (20/30 GHz) satellite systems, involving very small aper-
ture terminals (VSAT). At the Ka-band,propagation impairments strongly limit the
quality and availability of satellite communications. Adaptive impairment mitigation
techniques have to be used in order to improve link performance.Amongst all other
fade mitigation techniques,our interest is to implement adaptive modulation and cod-
ing technique.
8
Chapter 2
Fade Mitigation Techniques(FMT)
2.1 Link adaptation
As we all know that the link between between transmitter and receiver is wireless
in satellite communication and the future satellite communication is aiming to go for
higher frequency bands like Ka band.The use of the Ka band (30/20 GHz) for satel-
lite communication systems raises the problem of dealing with rain attenuation. As
opposed to the traditionally used Ku band (14/12 GHz), the Ka band is much more af-
fected by atmospheric events that lead to bad signal conditions, ranging from a slowly
changing attenuation of the signal to a sudden deep fade that blocks all communica-
tion.
The channel fades can be tracked / predicted then transmission signal may be de-
signed so as to avoid the fades / take advantage of good channel conditions. Such
types of systems are known as link adaptation systems where link level parameters
are dynamically adjusted in order to maximize the data rates over a certain period of
time.
2.1.1 BER performance for different modulation schemes
The bit error rate is used as the performance measure in satellite communication. The
bit error rate performance of different modulation schemes and coding rates are dif-
ferent.Some of the curves are shown fig. 2.1 In the fig. 2.1 the x-axis is EbNo and y
-axis represents the corresponding probability of errors.As we all know that as EbNo
increases the corresponding BER will decreases.The different curves are for different
code rates for QAM and considered with AWGN channel. When we calculate the
link budget we will come to know the amount of margin we have to operate.Suppose
for successful operation of the link the minimum probability of error required is 0.01,
then from the fade margin we will select the suitable modulation scheme. This kind
9
−8 −6 −4 −2 0 2 4 6 8 10 1210
−5
10−4
10−3
10−2
10−1
100
BER performance for M = 16modulation
Eb/No in dB
prob
. of e
rror
16−QAMAWGN
, 1/2 Conv.
16−QAMAWGN
16−QAMAWGN+RLY
16−QAMAWGN+RLY
, 1/2 Conv.
16−QAMAWGN
, 1/3 Conv.
16−QAMAWGN+RLY
, 1/3 Conv.
Figure 2.1: EbNo verses probability of error curves
of system is known as link adaptation system where system parameters are changed
according to the fade conditions.
If the EbNo is less then we will go for lower order modulation schemes but we need
to maintain the required probability of error. As the EbNo increases we can use the
higher order modulation schemes with different coding rates.
As an example consider the curves shown in fig. 2.2 taken from [7] in which the
BER performance of different modulation schemes and coding rates is given. If the
received SNR is below 8 dB none of the curves satisify the required BER, hence it is
better not to transmit anything during that time.If the SNR is between 8 and 10 dB
better to transmit with QPSK because it satisify the required BER. When it is between
10 and 15 dB both QPSK and 16-QAM with c=1/3 satisify the required BER but we
will use the higher order modulation scheme to send the data so that we can get the
more data rate. In this way the link is adaptively selected according to the SNR.
Inorder to avoid the fade we need to go for fade mitigation techniques.Before going
into fade mitigation techniques, first of all we need to know the different kinds of
impairments that are there in the atmosphere.Then we need to find the amount of at-
tenuation presented by each impairment so that we can find the overall attenuation
presented by atmosphere.There are many phenomena that lead to signal loss on trans-
mission through the earths atmosphere like Atmospheric absorption, Cloud attenu-
ation, Ionospheric scintillation, Tropospheric scintillation, and Rain attenuation.The
10
Figure 2.2: SNR verses BER curves taken from [7]
detailed study of these attenuation models has been presented in ITU documents.
2.2 Propagation effects and their impact on satellite-earth
links
Atmospheric absorption
At microwave frequencies and above, electromagnetic waves interact with molecules
in the atmosphere to cause signal attenuation. At certain frequencies, resonant absorp-
tion occurs and severe attenuation can result.The amount of attenuation is less than 1
dB on most paths below 100 GHz.
Cloud attenuation
Clouds have become an important factor for someKa-band paths and all V-band(50/40
GHz) systems. The difficulty with modeling cloud attenuation is that clouds are of
many types and can exist at many levels.The water droplet concentrations in each
11
cloud will also vary,and clouds made up of ice crystals cause little attenuation. The
amount of attenuation is 1 and 2 dB at frequencies around 30 GHz
Tropospheric scintillation
Energy from the sun warms the surface of the earth and the resultant convective activ-
ity agitates the boundary layer. This agitation results in turbulent mixing of different
parts of boundary layer, causing small scale variations in refractive index. The rapid
variations in refractive index along the path will lead to fluctuations in the received
signal level these fluctuations are known as tropospheric scintillations.
Low angle fading
When the elevation angle falls below 10 degrees, a second propagation effect becomes
noticeable that is low angle fading. Low angle fading is the same phenomena as mul-
tipath fading in terrestrial paths. A signal transmitted from a satellite arrives at the
earth station receiving antenna via different paths with different phase shifts. On the
combination, the resultant waveformmay be enhanced or attenuated from the normal
clear sky level.
Ionospheric scintillation
Energy from sun causes the ionosphere to grow during the day, increasing the total
electron content(TEC) by two orders of magnitude, or more. The rapid change in
TEC from the daytime to nighttime, which occurs at local sunset in the ionosphere,
that gives rise to irregularities in the ionosphere. These rapid fluctuations are called
ionospheric scintillations.
Rain attenuation
At frequencies above 10 GHz , rain is the dominant propagation phenomenon on
satellite links.Rain drops absorb and scatter the electromagnetic waves. In Ku and
Ka bands rain attenuation is almost entirely caused by absorption.At Ka band there is
a small contribution from scattering by large rain drops.Rain is the primary cause of
depolarization. Atmospheric gases and tropospheric scintillation do not cause signal
depolarization.Ionosphere causes the depolarization.Some of the energy in one polar-
ization can cross over to other polarization due to asymmetric particles in the existing
path, which leads to depolarization. Measure of depolarization that is most useful
in analyzing communication system is the cross pole isolation(XPI).It is the ratio of
wanted power to the unwanted power.
12
Once we come to know the amount of attenuation in the atmosphere we will calcu-
late the link budget. Link budget is nothing but the tabular method of calculating the
received power and noise power. When we calculate our link budget then we come
to know how much amount of margin we have and accordingly we will go for imple-
menting the fade mitigation techniques if necessary.
2.3 Fade Mitigation Techniques
As the operating frequency is increased, the atmospheric attenuations becomes more
severe. so implementing static margin as the only mean to compensate the propaga-
tion impairments at high frequency bands is not a good task, and it will push towards
the implementation of Fade Mitigation Techniques(FMT).Those techniques allow sys-
tems with rather small static margin to be designed, while overcoming in real time
cloud attenuation, some fraction of rain attenuation,scintillation, and depolarization
events.
Making use of FadeMitigation Techniques involves adapting in real time the link bud-
get to the propagation conditions through some specific parameters such as power,
data rate, coding etc. However, this real time adaptivity has an impact not only on
carrier-to-noise ratios but also on carrier-to-interference ratios and on upper layers.
Both aspects have therefore to be carefully studied. Various methods exist to counter-
act propagation effects at the physical layer level. The most relevant ones should take
into account operating frequency bands, performance objectives of the system and ge-
ometry of the network (system architecture, multiple access schemes).In fact FMT for
the physical layer can be divided into :
1. Power Control : transmitting power level fitted to propagation impairments,
2. Adaptive waveform : fade compensated by amore efficient modulation and cod-
ing scheme,
3. Diversity : fade avoided by the use of another less impaired link,
4. Layer 2 : coping with the temporal dynamics of the fade.
2.3.1 Power control
Four types of Power Control FMT can be considered : Up-Link Power Control (ULPC),
End-to-End Power Control (EEPC), Down-Link Power Control (DLPC) and On-Board
Beam Shaping (OBBS).Various ways of power control are explained in fig. 2.3
13
Figure 2.3: Various ways of power control
Up-Link Power Control (ULPC)
The aim of ULPC, the output power of a transmitting Earth station is matched to
uplink impairments. Transmitter power is increased to counteract fade or decreased
when more favorable propagation conditions are recovered so as to limit interference
in clear sky conditions and therefore to optimise satellite capacity. In the case of trans-
parent payloads, ULPC can prevent from reductions of satellite EIRP caused by the
decreased uplink power level that would occur in the absence of ULPC.
End-to-End Power Control(EEPC)
EEPC can be used for transparent configuration only. Indeed, the output power of a
transmitting Earth station is matched to up-link or down-link impairments. In the case
of regenerative repeaters, up and down links budgets are independent, so the concept
of EEPC can not exist anymore. EEPC is used to keep a constant overall margin of the
system. As for ULPC, transmitter power is increased to counteract fade or decreased
when more favourable propagation conditions are recovered to limit interference and
optimise satellite capacity.
14
Down-Link Power Control (DLPC)
WithDLPC, the on-board channel output power is adjusted to themagnitude of down-
link attenuation. DLPC aims to allocate a limited extra-power on-board in order to
compensate a possible degradation in term of down-link C/N0 due to propagation
conditions on a particular region. In this case, all Earth stations in the same spot beam
benefit from the improvement of EIRP.
On-Board Beam Shaping (OBBS)
OBBS technique is based on active antennas, which allows spot beam gains to be
adapted to propagation conditions. Actually, the objective is to radiate extra-power,
and to compensate rain attenuation only on spot beams where rain is likely to occur.
2.3.2 Adaptive waveform
These FMTs could be split into Adaptive Coding (AC), Adaptive Modulation (AM)
and Data Rate Reduction (DRR).
Adaptive Coding (AC)
The introduction of redundant bits to the information bits when a link is experiencing
fading, allows detection and correction of errors (FEC) caused by propagation impair-
ments and leads to a reduction of the required energy per information bit. Adaptive
coding consists in implementing a variable coding rate matched to impairments orig-
inating from propagation conditions.
Adaptive Modulation (AM)
Higher system capacity for a given bandwidth can be achieved with spectral efficient
modulation schemes but in clear sky conditions only due to link budget power limita-
tion. As Adaptive Coding, the aim of Adaptive Modulation is to decrease the required
energy per information bit required corresponding to a given BER, which translates
into a reduction of the spectral efficiency as C/N0 decreases. The reduction of the
spectral efficiency is the results of the use of lower level modulation schemes.
Data Rate Reduction (DRR)
Further reduction can be obtained by a decrease of the information data rate at con-
stant BER. The technique is called Data Rate Reduction. Here, user data rates should
be matched to propagation conditions : nominal data rates are used under clear sky
15
conditions (no degradation of the service quality with respect to the system margin),
whereas reductions is introduced according to fade levels.
2.3.3 Diversity
The objective of these techniques is to re-route information in the network in order
to avoid impairments due to an atmospheric perturbation. Three types of diversity
techniques can be considered: site (SD), satellite (SatD) and frequency (FD) diversity.
These techniques are very expensive as the associated equipments have to be redun-
dant.
Site Diversity(SD)
SD is based on the change of the network routes, therefore, it applies only for the Fixed
Satellite Service. SD takes advantage of the fact that two fades experienced by two
Earth Stations separated by a distance higher than the size of a convective rain cell (at
least 10 km), are statistically independent. The Earth station affected by aweaker event
is used and the information is routed to the original destination through a separated
terrestrial network.The concept is explained with the help of fig. 2.4
Satellite Diversity(SatD)
Satellite Diversity can be regarded in two different ways : on one hand, when de-
signing the system, by optimising the size of the constellation (that is the number of
satellites) in order to prevent communications at low elevation angles. On the other
hand in allowing Earth Stations to choose between various satellites, the one for which
the most favourable link with respect to the propagation conditions exists.
Frequency (FD) diversity
Frequency Diversity is a technique based on the fact that payloads using two different
frequency bands are available onboard. When a fade is occurring, links are re-routed
using the lowest frequency band payload, less sensitive to atmospheric propagation
impairments.
2.3.4 Layer 2
FMT at layer 2 level are techniques which do not aim at mitigating a fade event but
instead rely on the re-transmission of the message. Two different techniques can be
envisaged at layer 2 : Automatic Repeat Request (ARQ) and Time Diversity (TD).
With ARQ, the message is sent regularly until the message reaches successfully the
16
Figure 2.4: site diversity
receiver. ARQ with a random or predefined time repetition protocol would be an
alternate solution.
Time diversity can be considered as a FMT that aims to re-send the information when
the state of the propagation channel allows to get through. In this case, most often,
there is no need to receive the data file in real time and it is acceptable for the user point
of view to wait for the end of the propagation event (in general some tens of minutes)
or for a decrease of traffic. This technique benefits from the use of propagation mid-
term prediction model in order to estimate the most appropriate time to re-sent the
message without repeating the request.
17
Chapter 3
Implementation of FMT
3.1 FMT control logic
The aim of a FMT control loop is to track the variations of the propagation channel in
real time and to compensate propagation impairments either to increase its availabil-
ity or to improve its instantaneous performance. For this purpose, it is first necessary
to detect when a fade is occurring in order to assess if the quality of link is going to
be degraded or if an outage is going to occur. Secondly, whenever an event supposed
to lead to an outage is detected, it is necessary to check if the terminal is authorized
to set up the mitigation, and upon reception of the clearance, to trigger the mitigation
process. Another step can consist in performing a real time prediction of the propaga-
tion channel in order to compensate the reaction time of the system to obtain a better
control loop behaviour.The system block diagram is explained with the help of fig. 3.1
where the transmitter will send the beacon signal.The receiver gets the transmitted
signal and sends back the channel quality through the feedback.accordingly the link
parameters will be changed to counteract the fade.The basic FMT logic is explained
with the help of fig. 6.1
3.2 Implementation of FMT
The electromagnetic waves undergo power decrease, scattering and depolarization
during propagation through rain and clouds. The attenuation due to rain is the main
factor that influences the performance of a high-frequency satellite communications
link. This results in a decrease of the percentage of the time for which a satellite link
can be expected to operate with a specified bit error rate (BER). Consequently, atmo-
spheric impairments affect availability and throughput of the communication system.
In order to overcome the adverse effects of atmosphere and to improve the reliability
of communication system we should control the parameters of system. This can be
18
Figure 3.1: System block diagram
Figure 3.2: Block diagram of FCM
accomplished by a fade countermeasure system. The block diagram of a fade counter-
measure system is shown in fig. 3.2
In the block diagram transmitting part constitutes the error control coding and bit to
symbol mapping. These transmitted signal is multiplied by the channel coefficients
and added by the noise. The receiving part will detect the faded symbols and de-
modulate to get the original bit stream. The knowledge of channel conditions can be
19
obtained by observing the primary parameters like attenuation, statistical distribution
of rain or secondary parameters like signal to noise ratio, BER, received signal char-
acteristics. Once we know the amount of fading present in the channel(by detection)
at time ’T’ we will try to predict the channel information at T+t. If we can predict the
channel information well in advance we can make a decision to adapt the coding and
modulation according to channel information.
3.3 Description of simulator
3.3.1 CRC Encoding
The data-bits are padded with the CRC bits. The CRC polynomial used for this pur-
pose is 0x04C11DB7, which is the IEEE 802.3 standard for Ethernet links. The basic
idea is to take each packet of data bits at a time, add 32-bit CRC code to it and give this
modified packet to the modulator to be sent across the link.
3.3.2 Error Control Coding
The channel coding is used for the reliable transmission of digital information over
the channel. The error control coding techniques rely on the systematic addition of re-
dundant symbols to the transmitted information so as to facilitate two basic objectives
at the receiver i.e. error detection and error correction. The channel encoder accepts
message bits and adds redundancy according to a prescribed rule, thereby producing
encoded data at a higher bit rate. The channel decoder exploits the redundancy to
decide which message bit was actually transmitted. The channel goal of the channel
encoder and decoder is to minimize the effect of channel noise. That is, the number
of errors between the channel encoder input and the channel decoder output is mini-
mized.
3.3.3 Modulation
Modulation is defined as the process by which some characteristics of a carrier is var-
ied accordance with a modulating wave. In digital communications, the modulating
wave consists of binary data or an M-ary encoded version of it. There are different
types of modulation schemes available but the final priority is determined by the way
in which the available primary communication resources, transmitted power and the
channel bandwidth, are best exploited. The raw bits are converted into symbols and
the number of bits in each symbol varies according to the type of modulation used. If
20
0 200 400 600 800 1000 1200 1400 1600 18003.5
4
4.5
5
5.5
6
6.5
7
Time in sec
SN
R
Channel SNR
Figure 3.3: variation of channel with time
the modulation scheme is BPSK then we will have one bit per symbol and for QPSK it
will be two bits per symbol and for 16 QAM it will be four bits per symbol.
3.3.4 Channel
The transmitted signal(bits) is to be transmitted through the channel so modeling of
the channel has to be done. channel will attenuate the signal. The transmitted signal
is multiplied by the channel coefficient. The small fluctuations in the channel will lead
to fading. Hence to overcome this fading we need to implement the fade mitigation
techniques. Here we have got the received signal strength for some days. The data has
been collected for every 16 seconds that is the channel coefficient is changing for every
sixteen seconds. The variations of the channel with respect to time for one particular
day has been plotted.
3.3.5 Automatic Gain Control (AGC)
Automatic gain control (AGC) is an adaptive system found inmany electronic devices.
The average output signal level is fed back to adjust the gain to an appropriate level
for a range of input signal levels. For example, without AGC the sound emitted from
an AM radio receiver would vary to an extreme extent from a weak to a strong signal;
the AGC effectively reduces the volume if the signal is strong and raises it when it is
weaker. AGC algorithms often use a PID controller where the P term is driven by the
error between expected and actual output amplitude.In the simulation, AGC is not
21
being used for the time being.
3.3.6 demodulation
The received symbols which are in the form of constellation are converted to bits by
symbol de-mapping. Hence, the bits are extracted from the symbols.
3.3.7 Decoding
In this block, the received bits are decoded .The decoding techniques rely on the sys-
tematic removal of redundant symbols that were padded to the transmitted bits so as
to facilitate two basic objectives at the receiver error detection and error correction.
3.3.8 CRC Decoder
At the receiver end, the received bits at the end of the demodulator block are taken one
packet at a time, the CRC code added at the transmitter is stripped off and the packet
is checked for errors. The total number of erroneous packets is, thus, kept track of.
The channel,detection and the decision algorithms are described in the subsequent
chapters.
22
Chapter 4
Channel
To study the characteristics of the channel is the most primary requirement for model-
ing the same. Study of the existing literature has not yet led to a strong conclusion in
this matter. Excerpts from some of the references follow:
Reference [9]: The model describes that the rain attenuation probability distribution
is predicted dependent on two sample values measured shortly earlier. The model
equations and its parameter values have been derived empirically from measurement
results. For a signal with a sampling time of 10 seconds, the probability distribution
function of the attenuation of any sample is predicted as the ’hyperbolic secant distri-
bution’. This model shows the spectral characteristics of short-term dynamic during
a rain attenuation event are well described. The synthesized time series exhibit a de-
creasing power spectral density when the event duration increases, which is caused
by the procedure to obtain the desired maximum event attenuation is shown in this
paper.
Reference [10]: This paper has described the standard deviation of attenuation as the
function of the previous two value of attenuation. Once the average and standard de-
viation of next rain attenuation is known, its probability distribution function can be
plotted a short time after a measured value.
Reference [11]: This model provides the relationships between the parameters .The
dynamic model is based on the log normal distributions of the rain attenuation and
utilizes a non-linear device to transform attenuation and rain intensity into a one-
dimensional Gaussian Stationary Markov process. So, the rate of change of attenua-
tion, mean value of attenuation and the variance are taken care of in the simulation.
During transmission through the channel, the signal gets attenuated due to the signal
amplitude fluctuation that leads to fading. To overcome the problem, fade mitiga-
tion techniques need to be implemented. As the first step to proceed in the domain
of channel modeling, the measured data regarding the received signal strength were
collected for a few days. The data has been collected for every 10 seconds which is
also considered as the sampling time in the simulation. That is, the channel coefficient
23
is changing every ten seconds.
In the simulation, the channel is modeled as a slow-varying Rayleigh channel with
the negligible Doppler effect. The output of the channel is taken to be the attenuation
values in the simulation. The noise power also varies according to these attenuation
values, due to coupling of the increased sky noise due to rain into the receiver an-
tenna. The noise power Pn in watts [4] in this case is a function of attenuation. The
noise power is given by
Pn = KTsBn (4.1)
where K = The Boltzmann’s constant = -228.6 dBW/K/Hz
Ts = The physical temperature of the source in Kelvin degrees
Bn = The noise bandwidth in which the power is measured in Hertz
The total path attenuation A(dB) is the sum of the clear sky attenuation due to atmo-
spheric gaseous absorption A(clear air) and the attenuation due to the rain,A(rain)
A = Aclearair + AraindB (4.2)
The sky noise temperature T(sky) resulting from the total path attenuation A (dB) is:
Tsky = 270(1 − 10−A/10)K (4.3)
where 270 K is the assumed temperature of the medium due to rain [9].
The antenna noise temperature T(antenna) is calculated by multiplying the coupling
coefficient (n of 90-95 percent) with T(sky). Thus,
Tantenna = nTskyK (4.4)
As the satellites use a high gain LNA, the contribution of the later parts of the receiver
to the system noise temperature becomes negligible. The system noise temperature
T(system) is:
Tsystem = TLNA + TantennaK (4.5)
From the above expressions, it is found that the temperature varies with the atten-
uation. As the attenuation increases the noise temperature increases that in subse-
quently increases the noise power. This concept has been incorporated in the simula-
tion, where with the change in attenuation, the noise power is varying.
24
Chapter 5
Detection
5.1 Measurments
The amount of attenuation present in the channel is measured through the measure-
ment block. Measurments is nothing but the detection of attenuation. The detection
function has for objective to quantify precisely themagnitude of a fade event occurring
on the considered link. Three kinds of detection concepts can be identified: open-loop,
closed-loop and hybrid-loop .
5.1.1 Open-loop Detection
The open-loop detection concept relies on the estimation of uplink (or downlink) im-
pairment from a measurement of the propagation conditions. This measurement can
be carried out in several ways: rain intensity and other meteorological measurements,
sky brightness temperatures measured with a radiometer, radar networks, satellite
imagery or satellite beacon operating at uplink or downlink frequency.
5.1.2 Closed-loop Detection
In the closed-loop detection concept, estimation of the impairment is performed from
the measurement of the overall link performance. Bit Error Rate or Carrier plus Noise
estimations can be carried out by the earth station [6] or by the satellite (if On-Board-
Processing enables so). In the case of a transparent satellite link, a measurement of the
overall link will give information on the total degradation of the propagation channel.
However, it will not identify if the impairment is occurring on the uplink or on the
downlink.
25
5.1.3 Hybrid-loop Detection
To separate uplink and downlink fade contributions the hybrid-loop detection concept
uses two different measurements, one of them from a beacon and the other from the
link .
5.2 Detection
Estimation of the SNR is the first step in the operation of a FMT system. We propose
to estimate the SNR using the PER. Since the modulation and code-rate is known be-
forehand while the link is in operation, an estimate of the PER will allow us to get an
estimate of the current SNR. Using this value of the SNR, we can go on to decide the
optimum ACM scheme to use in the particular situation. We propose to estimate the
SNR using CRC32 to detect packet errors.
The CRC polynomial used for this purpose is 0x04C11DB7, which is the IEEE 802.3
standard for Ethernet links. The basic idea is to take each packet of data bits at a time,
add 32-bit CRC code to it and give this modified packet to the modulator to be sent
across the link. At the receiver end, the received bits at the end of the demodulator
block are taken one packet at a time, the CRC code added at the transmitter is stripped
off and the packet is checked for errors. The total number of erroneous packets is,
thus, kept track of.
We have implemented this in the simulator in a similar fashion. The built-in MATLAB
functions generate() and detect() have been used for this purpose. The generator and
detector objects required for the function to work are created before starting the sim-
ulation using the crc.generator() and crc.detector() functions. For each channel value,
the data bits to be sent are passed to the generate() function in the form of a column
vector and the function adds the CRC code at the end of this vector. These bits are
then sent modulated and noise of appropriate power is added to model the channel.
After being received at the demodulator and then, decoded, the data bits are passed
to the detect() function which returns the data bits sans the CRC bits and a variable
which indicates whether or not the packet contained an error. The number of erro-
neous packets received is counted using an accumulator variable.
An important point requiring attention is that the PER found out in this manner is
used to estimate the SNR and decide the optimum ACM scheme only in the next it-
eration of the simulation. This has been done to model the delay typically associated
with earth-satellite links which are generally of the order of 500 ms. In our simulation,
we have assumed that the channel varies considerably only after 10 seconds. So, data
corresponding to 10 seconds is processed during each iteration of the main loop and
the total number of packet errors during these 10 seconds is calculated. This PER is
26
used to estimate the SNR and decide the optimum ACM scheme in the next iteration.
Thus, a channel-information feedback delay of 10 seconds is automatically included in
the simulation. We plan to study our system using various values of feedback-delay
in the future.
5.3 Decision
Using the information of the predicted attenuation, this function will decide if the
considered link performs according to specifications and accordingly it will select re-
sources. The decision part is clearly explained in the next chapter.
5.4 Adaptive system
The adaptive system constitute the detection and decision part where we need to de-
tect the amount of fade and take the decision whether we need to activate FMT or not.
This has to be done adaptively that is based on the value of channel condition the sys-
tem has to adapt its resources for example it has to change its modulation and coding
rate if ACM is the chosen fade counter measure. By using the adaptive system we can
achieve higher data rate providing the required bit error rate.
27
Chapter 6
Decision
Implementation of fade mitigation techniques will constitute three parts one is detec-
tion of the attenuation present in the channel at time T, second is prediction of the
channel coefficient at time T+t based on the detected value at T, third is the decision in
which one need to take the decision whether we need to activate the fade mitigation
technique or not and also the level of fade mitigation technique is also to be decided.
The FMT control logic is given in fig. 6.1.
Figure 6.1: FMT control logic
28
6.1 Decision
Using the information of the predicted attenuation, this function will decide if the
considered link performs according to specifications that is
Eb
N0
≥Eb
N0
|Required (6.1)
for a given BER, modulation and coding scheme. Where
Eb
N0
=C
N0
⋆1
Rb
(6.2)
Where Rb is the information data rate. the above expression is equivalent to
Systemmargin ≥ 0 (6.3)
where
Systemmargin ≡Eb
N0
≥Eb
N0
|Required (6.4)
for a given modulation and coding scheme.
In clear sky conditions, equation (2.3) will be met. However during a fade event this
may not be the case, and therefore a Fade Mitigation Technique shall be activated.
This technique will vary one of the clear sky systems parameters transmitted power,
antenna Gain, data rate, coding etc.
6.1.1 Detection margin and Hysteresis
When attenuation is present in the link, equation 3.3 will determine whether the acti-
vation of the FMT is needed. However, to cope with possible estimation and predic-
tion errors, an additional margin can be included in the link budget equation
Systemmargin ≥ Controllogicmargin (6.5)
When system margin goes below the Control logic Margin Threshold, the Fade Miti-
gation Technique will be activated by changing the value of the associated parameter.
The new value will correspond to the smallest activation level that meets equation 2.5
The same procedure is followed to increase the level when FMT is already activated.
If the state of our channel is close to the detection threshold, small fluctuations may
cause the FMT switch from one level of activation to the following. Frequent switch-
ing have a negative influence on the overall system performance and they increase the
29
Figure 6.2: FMT intelligent system taken from [8]
amount of FMT signalling data required if FMT decision is not done locally. One way
of minimizing this problem is adding an hysteresis margin that will be apply when
the systems switch to a lower FMT level:
Systemmargin ≥ Detectionmargin + Hysteresis (6.6)
Accordingly we have to take the decision.In this way the decision is taken to counter-
act the fade.
6.2 Decision making algorithm
6.2.1 fade control
Fade control refers to the necessary operations that need to be undertaken so as to de-
ploy efficiently the fade countermeasure system based on detected attenuation. This
control scheme should be designed to meet short and long term design specifications
which relate directly to meaningful QoS metrics for Satellite Communication systems.
30
Most of the research has concentrated on Fade Control on a per-link basis. This implies
a distributed management of FMT resources, whereby individual active stations esti-
mate their FMT needs and then inform their gateway station of any change in commu-
nication settings due to atmospheric conditions via a suitable signalling. Fade Control
first must determine the full impact of the characteristics of the current rain event on
the QoS metrics. This may require determination of the, Current Level of rain/cloud
attenuation, Likely duration of the event, Likely short-term variations of the attenu-
ation, Likely interfade duration once an event has finished, Likely peak attenuation.
The corresponding diagram is shown in fig. 6.2.
One main aspect is that not only we are interesting in the actual level of fading but
in order to make more judicious decisions, it is also important to have a clear idea on
the type of rain event that is actually affecting the link(s) especially when the FMT
resources are shared among a set of users. Based on detected attenuation, the first
step is to determine the likely peak (to verify that the dynamic range is adequate),
fade/Inter-fade duration (for allocation of shared FMT resources) as well as a short-
term predicted level of attenuation (for response time of FMT). This is fed into a FMT
decision unit which compares the estimated quantities with a set of objectives (e.g.
thresholds) and tries and determine the required level of FMT. Provided resources are
available, the FMT is then deployed otherwise there will be an outage. in order to
make the decision we need to have a decision making algorithm. The flow chart for
decision has been presented in fig. 6.3.
31
Figure 6.3: Decision making flow chart
32
Chapter 7
Results
7.1 BER performance for different modulation schemes
The bit error rate is used as the performance measure in satellite communication. The
bit error rate performance of different modulation schemes and coding rates are dif-
ferent. Some of the curves are shown fig. 7.1 In the fig. 7.1 the x-axis is SNR and y
-axis represents the corresponding probability of errors. As we all know that as SNR
increases the corresponding BER will decreases. The different curves are for different
code rates for QAM, QPSK and BPSK and considered with AWGN channel.
−2 0 2 4 6 8 10 12 14 16 18
10−4
10−3
10−2
10−1
BER performance for 4−QAM(similar to QPSK), 16−QAM and 64−QAM
SNR (in dB)
BE
R
BPSK simulatedBPSK theoreticalQPSK simulatedQPSK theoretical16−QAM simulated16−QAM theoreticalBPSK 1/2 rate codeBPSK 1/3 rate codeQPSK 1/2 rate codeQPSK 1/3 rate code16−QAM 1/2 rate code16−QAM 1/3 rate code
Figure 7.1: SNR verses probability of error curves
33
7.1.1 Change of modulation scheme and coding rate with respect to
time
Based on the value of channel conditions the modulation scheme and coding rate
should be changed. If the channel condition is poor we will go for lower order mod-
ulation schemes and if the channel condition is good then we will go for higher order
modulation schemes.
As the time varies the channel condition changes hence the bit error rate will also
changes. The change in BER at different point of times is shown in fig. 7.3. The thresh-
old BER that we have set was 1/10 and when we observe the simulated BER most of
the times it is below the target threshold if we use adaptive modulation and coding.
In the result we have shown the bit error rate for different modulation schemes. If we
use 16 QAM with 1/2 code rate we will achieve higher bit rate but we can observe
from the result that most of the times its bit error rate is above the threshold. Hence
if we use this constant rate transmission scheme then we will have outage most of the
times, which is not desirable. Same is the case with 16 QAM with rate 1/3. If we use
QPSK with code 1/2 and QPSK with no code then we can achieve the required bit
error rate but the data rate for these schemes will be less. So to achieve the higher data
rate and the required bit error rate we will go for adaptive coding and modulation,
that is based on the channel conditions we will change our modulation scheme and
coding rate. If the channel condition is poor then we will use lower order modulation
schemes and if the channel condition is good we will go for higher order modulation
schemes. the result is shown as BER, AMC in fig. 7.3. Hence we can conclude that by
using adaptive modulation and coding rate we can improve the data rate providing
the required bit error rate.
7.2 BER performance for the collected data
So far we have been simulating the system with the random generation of channel
coefficients. Now we have simulated the system for the collected data that is the rain
attenuation data has been collected for several days and experiments have been per-
formed on these data. Basically the results have been presented for two days one is on
5th where we have huge amount of rain and second is on sixth where we do not have
any rain attenuation.
Actually the detection part has to detect the amount of fade present in the atmosphere
but as of now we have not proposed any detection algorithm hence we have using the
rain data for simulation. The measurement of beacon signal have been given that is
the signal strength value is given based on which we need to take the decision. Based
34
0 200 400 600 800 1000 1200 1400 1600 18000
2
4
6
8
10
12
14
16
Time in sec
SN
R
Channel SNRM values chosenC values chosen
Figure 7.2: change of modulation and coding with time(date 6th)
on the values of signal strength we have selected the modulation scheme and coding.
In the figure. 7.2 we can observe that as the channel condition is changing the mod-
ulation and coding rate are also changing. The bit error rate for different modulation
schemes with respect to time is presented in the figure. 7.3.
In figure. 7.4 we can observe that during 60 -80 sec there is a heavy rain because of
which the signal strength goes down which in turn increments the BER. By using
ACM we can send the data but even then we will not have the enough margin to
overcome this outage. During that time we can go for some other FMT’s like power
control in which we will increase the transmit power or site diversity in which link is
made available through some other terrestrial link. joint FMT’s will further improve
the system performance.
7.2.1 PER performance for different SNR
With respect to the figure 7.6,it is seen that,when the SNR is high,the per is less and the
higher order modulation schemes and coding rates are chosen.But, when the channel
condition is worse, the lesser modulation schemes and coding rates are applied so as
to send as much less no. of bits as possible.
7.2.2 Performance of the FMT simulator with time
The estimated SNR almost tracks the calculated SNR most of the time. Based on the
estimated SNR,the decision of adaptive modulation and coding scheme is taken.Th
corresponding diagram is shown in 7.7
35
0 200 400 600 800 1000 1200 1400 1600 180010
−4
10−3
10−2
10−1
100
Time in sec
BE
R
M=4,C=1M=2,C=1M=16,C=1M=16,C=2BER, AMCThreshold
Figure 7.3: change of ber with time (with collected data(date 6th))
0 20 40 60 80 100 120 140 160 180−20
−15
−10
−5
0
5
10
15
20
Time in sec
SN
R
Channel SNRM values chosenC values chosen
Figure 7.4: change of modulation and coding with time(date 5th)
36
0 20 40 60 80 100 120 140 160 18010
−4
10−3
10−2
10−1
100
Time in sec
BE
R
M=2,C=1M=4,C=1M=16,C=1M=16, C=2BER, AMC
Figure 7.5: change of ber with time (with collected data(date 5th))
Figure 7.6: change of modulation and coding with time(date 5th)
37
Figure 7.7: change of modulation and coding with time(date 5th)
38
Chapter 8
Practical Implementation with Modem
(SRM6100)
8.1 About the Modem-SRM6100
1. This modem is a transceiver modem with a high performance wireless radio de-
signed for heavy-use industrial data communications in the range of 2.4-2.4835
GHz .
2. Advanced frequency hopping and error detection technology to provide data
integrity
3. The modem has datarates of 1200-234.4kbps.
4. The operating range of the modem is in the range of 24 km in optimal conditions
of line-of-sight[12].
8.2 Experiments to be done with the modem SRM6100
1. To measure the unlock-to-lock delay performance of the modem.
2. Implementation of the FMT algorithms and verification of the same.
8.3 Experiments done with the modem SRM6100
1. Performance of the loop back bench test:
In this test,the data was transmitted from one transmitter Pc to the Receiver by
the hyperterminal.The test was done by following the instructions from theman-
ual.In this case, the receiver terminal was shorted with the transmitter termi-
nal,so that what ever data was being transmitted was received at the same time.
39
2. Interfacing with the matlab :
Here,we have send data through the code and have received the same at the re-
ceiver end by the matlab.In this case the short was removed and the two modem
were connected to two different computers,as tranmitter and receiver respec-
tively.
3. Configuration of the modem:
The configuration of the modem is also being done with the matlab code.At the
same time, the data can be sent through the code and received on the other side
and the configuration can again be changed to do the same.
In the configuration, we have different options like changing the transmit power
or changing the data rate.
Firstly, the datawill be transmittedwith some fixed data rate and transmit power.
Secondly we change the configuration settings of the modem to send the data
with different transmit power.
4. Limitations:
(a) Themodem should always be triggeredmanually to show the configuration
window at the hyperterminal,even though we program it through matlab.
(b) There are not enough options in SRM6100 to change themodulation scheme
and coding rate in accordance with the channel.
(c) The actual data rate is not known to the user and thus there are not many
data rate options.
Hence, we would like to perform the experiment with different satellite modem.
The plan of the experiment has been given in the following chapter
40
Chapter 9
Plan of the experiment
9.1 Objective of the experiment
To measure the unlock-to-lock delay performance of the high-speed satellite modem
9.2 Scope
1. The modem used in the experiment is the CDM-700 Satellite Modem.
2. We will test the unlock-to-lock delay, mainly, by changing the Modulation and
Coding (M,C) scheme. This will be done by giving the corresponding command
to the two modems. We will also try to study the effect of changing the attenua-
tion value in the attenuator on the delay value.
3. The switching delay occurring while sending data to the Tx-modem and getting
data from the Rx-modem will be considered negligible for the purpose of our
experiment. Basically, we will try to find an upper-bound to the time the system
takes to change the M,C scheme and get ready to send and receive data.
9.3 Experimental Set-up
The following connections are necessary:
1. The data-bits to be transmitted are sent from the computer to the transmitter-
modem
2. The Rx-modem sends the total received bits to the computer for BER calculation
3. Connection for the attenuator with the computer
4. Connection for transmitting the M,C decision
41
Figure 9.1: plan of the experiment
5. Connection to the receiver-modem regarding the M,C that has been selected at
the transmitter-modem.
6. IF/RF Connection for transmission of the modulated bits from the transmitter to
the attenuator.
7. IF/RF Connection for transmission of the modulated bits from the attenuator to
the receiver modem.
9.4 Methodology
1. The computer is the information source in our experiment. The data is gener-
ated as a random sequence of bits and is sent to the Transmitter-modem by data
interface (1).
2. Initially, the experiment is started with a pre-defined modulation and coding
rate. This information is given to the transmitter modem (4) and receiver modem
(5) by the computer. Afterwards, the modulation and coding rates are selected
based on the BER calculation.
3. The attenuator models the varying channel in the entire experiment. In the above
block diagram, the attenuator is programmable by the system. The transmitted
42
bits are attenuated by the attenuator block (3). In case, a programmable attenu-
ator is not available, a manual attenuator will also do. But in that case, we will
have to change the attenuation values manually by hand. If an attenuator is not
available at all, we just have to send the transmitted signal to the receiver. Nat-
urally we would not be able to study the effect of channel attenuation on the
unlock-to-lock delay in this case.
4. The received data-bits are transferred from the receiver-modem to the computer
(2).
5. Based on the BER results, the M,C are changed. This change is intimated to
the two modems which subsequently change their M,C schemes. When the
”change” message is sent to the two modems, two counters are started - one
for the TX-modem and the other for the Rx-modem. The two modems are then
queried continuously to find whether they have been able to lock or not. When
any of the two is locked, the corresponding counter is stopped and the time-
delay is noted down.
6. Steps 1-5 are repeated for a sufficient number of times for finding the delay per-
formance of the modem.
9.5 Resources required
1. Computer
2. Transmitter and Receiver Modems - both CDM-700 Satellite Modems
3. Programmable Attenuator interfaced to the computer (optional)
4. Data interface (for the two modems)
9.6 Expected Results
1. Estimation of the average and maximum values of delay between sending the
M,C scheme information to the modems and getting the modems ready to send
and receive data after M,C switching.
2. Finding whether changing the channel attenuation has any effect on the unlock-
to-lock delay. If it has, what is the effect like?
3. Implementation of the FMT loop operating in real-time. This will be possible
only if the attenuator is programmable using the computer.
43
Chapter 10
Plan and Budget of project
10.1 Plan of Project
• The development plan:
1. The Development of the fade detection algorithms:
The fade detection algorithms development largely depends on the devel-
opment of the channel model. according to the channel model parameters ,
we can get an fair idea of how the channel is varying and the probability of
the signal to be in fade for the next subsequent time.
2. The modem interface:
The interfacing has being done by the modem SRM6100, just to realize the
working of the whole system but the idea is to interface with the satellite
modem ,where the real-time data can be transferred.
• The refinements:
1. The Channel model is to be refined from the aspect of selecting some pa-
rameters, which will help the development of the fade detection algorithms
mentioned earlier.
2. The SNR estimation is to be improved
3. The data rate selection can also be improved based on the estimation of PER.
44
10.2 Budget of Project
Head Amount
Salary 1,20,000Equipment 2,50,000Books ContingenciesOutsoursing 15,000Travel 1,00,000Consumable 15,000SRIC Overhead 15% 75,000Total 5,75,000
45
Chapter 11
Conclusion
Inorder to implement the fade mitigation techniques in satellite communication at
higher frequency bands first we need to know the various kinds of impairments and
their impact on the satellite links. Then link budget will be calculated and accordingly
we will find the amount of margin we can get. Once we know the margin then we
have to decide whether we need to activate the fade mitigation technique or not. In-
order to do this we need to design the FMT control loop. Then we can activate the
FMT’s as and when needed to have a good performance of the link. The adaptive cod-
ing and modulation is simulated and results are presented. the results show that by
using ACM we can improve the data rate providing the required bit error rate.
From the results we can observe that adaptive modulation and coding works well but
it may not remove the outage everytime. Some times we need to use the other FMT’s
when there is not enough margin for ACM. Combination of different FMT’s will fur-
ther improve the system performance.
46
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48
