1. Cognitive Radio-Based Geostationary Satellite
Communications for Ka-band Transmissions
Paulo Victor R. Ferreira, Rushabh Metha and Alexander M. Wyglinski
Wireless Innovation Laboratory, WILab
Department of Electrical and Computer Engineering
Worcester Polytechnic Institute, WPI
Worcester, MA, USA
{prferreira, rmmehta, alexw}@wpi.edu
Abstract—This paper proposes an adaptive modulation scheme
using rain fading predictions obtained via Kalman filtering
in order to mitigate the effects of rain on cognitive radio-
based geostationary (GEO) satellites operating in the Ka-band.
In the proposed scheme, the need for adaptation is identified
prior to the rain attenuation event, allowing for enough time
for the transmitter and receiver to reconfigure, which is a
requirement when one of the communicating nodes are moving
at a certain relative speed. We show that the bit error rate (BER)
performance can be improved by two orders of magnitude for
a system that accounts for the overall delay when adapting its
modulation scheme based on the proposed predictor outputs.
Index Terms—Satellite communication, cognitive radio, predic-
tion methods, rain fading
I. INTRODUCTION
The increasing number of Internet users and the usage of
applications that require high data rate satellite communication
links have motivated network operators to start operating at
higher frequency bands possessing greater spectrum availabil-
ity, such as Ka-band, which operates between 26.5 GHz and 40
GHz [1]. However, bands such as the Ka-band are susceptible
to weather impairments, which can significantly attenuate the
transmitted signal.
The attenuation caused by rain, also known as rain fading,
has a physical characteristic of absorbing the energy of an
electric field being transmitted through it [2]. It varies ac-
cording to the transmitted signal frequency, receiver antenna
longitude and elevation angle, rain rate in mm/h, and other
factors. Thus, depending on the modulation scheme used and
the local amount of rain, the communication link performance
can vary.
Researches seeking to mitigate rain attenuation effects in
GEO satellite usually proposes adaptive modulation and cod-
ing (ACM) techniques that reconfigure some radio parameters
of both transmitter and receiver [3]–[8]. The receiver measures
the channel attenuation level and reports it to the transmitter
via a feedback channel, which will reconfigure its radios
accordingly. Although ACM is not a new concept, the majority
of these researches ignore the effects of the delay experienced
by the feedback information that can occur if one of the
communicating nodes are moving at a certain relative speed.
In this paper, we propose an adaptive modulation scheme for
cognitive radio-based GEO satellite communication systems
Fig. 1. Illustration of a full-duplex communication link between two ground
stations through a GEO satellite. Links A and B are under rain fading.
based on predictions of rain fading, the major attenuation
source at Ka-band frequencies. We consider that the path loss
is known and that any additional attenuation is due to the rain.
The objective is to optimize the goodput during periods of
rain precipitation according to the predicted attenuation level
allowing for enough time for both the transmitter and the
receiver to reconfigure their radios just before that predicted
attenuation level starts affecting the signals being transmitted.
Thus, by the time of the rain event the transmitter and the
receiver would have already reconfigured their radios in order
to deliver the best data rates possible.
This paper is organized as follows: In Section II, we
describe the communication system configuration and its sub-
systems. In Section III, we analyze the performance of the
system under four different scenarios. In Section IV, we make
conclusions about the results and make some comments about
the next steps regarding improvements and future application
extensions.
II. SATELLITE COMMUNICATION SYSTEM SIMULATION
TEST-BED
In order to simulate the prediction and adaptation functions
of our proposed scheme, we developed a communication
system in MATLAB consisting of a transmitter, a receiver, and
a channel. The transmitter generates random information and
modulates it. All the modulation schemes used in this paper
are uncoded and we assume that the system is completely
synchronized. Given that we have a bandlimited channel, we
consider a flat noise power and thus we can model the GEO
satellite channel as an additive white Gaussian noise (AWGN)
978-1-4799-7088-9/14/$31.00 ©2014 IEEE
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2. channel [9]. The receiver is the part of the system responsible
for processing the improvements that this paper proposes.
Besides performing the demodulation of the received signal,
it also performs the predictions that are fed into decision logic
for deciding if any radio parameters need to be changed based
on the link performance requirements previously set by the
network manager. The following subsections describe in detail
the operation of each of these sub-systems.
In satellite communications, we usually have link estab-
lished between two ground stations through one satellite in
the most simple case. Thus, there can be two full-duplex links,
i.e., two pairs consisting of uplink and downlink channels each
operating at different frequencies at the same time, as shown
in Fig. 1. The situation of both up- and down-links using the
same frequency at different time slots is not in the scope of
this paper.
The simulations done for this paper consist of one full-
duplex link (such as the A-B pair or the C-D pair in Fig. 1)
and we implemented the improvements for only one direction
of this link (e.g., A, B, C or D in Fig. 1), using the other
direction as feedback. We do not distinguish between whether
the ground station or the satellite is the receiver. Therefore,
we simply assume that the receiver will control the radio
parameters of the link of its receiving frequency. In the future
we plan to test this concept by implementing the improvements
at both ground station and satellite receivers.
Fig. 2 shows the diagram of the communication system
using the closed-loop link for control feedback. Suppose we
consider the A-B link in Fig. 1. If the proposed receiver
improvement is implemented in the ground station then the
attenuation to be predicted is done for the link B while the
link A will be used as a feedback. The requirements for this
type of adaptation scheme is that the transmitter includes the
power level being used for transmission on the header, as well
as the modulation being used for the current frame. In this way
when the receiver decides that the modulation scheme needs
to be adapted it sends the command to the transmitter via
feedback channel.
Fig. 2. Satellite simulation testbed block diagram.
A. BER Curves for Calibration
Fig. 2 shows the receiver sub-systems diagram blocks. The
system has a calibration phase which is run under normal
weather conditions, i.e., clear sky conditions, for the acqui-
sition of the BER curves for all the possible combinations of
the reconfigurable radio parameters.
These curves will allow the system to find the Eb/N0 values
that triggers the modulation adaptation, based on the maximum
allowed BER. For simplicity, in this paper we analyze two
uncoded modulation schemes: 4-QAM and 16-QAM. Their
BER curves for “clear sky” conditions are shown in Fig. 3 as
empirical results from the simulations compared against the
theoretical curves. The curves acquisition were made with the
concern that we get at least 100 errors.
Fig. 3. BER curves for clear sky conditions acquired during the calibration
phase compared against the theoretical curves for an AWGN channel
B. Prediction using discrete Linear Kalman Filters
GEO satellites orbit height is around 36,000 Km. The
round-trip propagation delay is close to 500 ms between two
ground nodes, plus the latency delay due to processing in each
communicating node. In scenarios where the receiver is at a
fixed position, the total delay, propagation plus latency, does
not represent a significant issue because during rain fading, just
the rain layer might be moving at almost constant speed, and
its speed does not change very quickly. However, if the ground
station is moving at a certain speed through regions where it
is raining, the total delay can play an important role. The
rate of change of the attenuation slope will vary accordingly
to the speed of the moving node and the current local rain
conditions. Thus, the channel state information will always
be outdated. Therefore, we propose the usage of attenuation
prediction using outdated measurements, where the current
node speed may dictate how far the predictor should forecast.
Reference [10] made some interesting analysis on the impact
of the node speed on the received SNR.
In order to predict the attenuation k-steps ahead we use the
linear Kalman Filter without control (1)-(5), [11]–[13]. The
prediction equation set projects the estimated state matrix ˆX
(1) and P (2), representing the error covariance between the
measurement and the changing rate, one time instant ahead.
These use the state transition matrix F (10) and the process
covariance matrix Q (13)-(14), detailed in Appendix A.
ˆXt|t−1 = Ft
ˆXt−1|t−1 (1)
Pt|t−1 = FtPt−1|t−1FT
t + Qt (2)
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3. The update equation set computes the Kalman gain (3) and
updates the estimations of the state matrix (4) and the error
covariance matrix (5). They use the measurement mapping
matrix H (11), the noisy measurement matrix y (12), and the
measurement noise R, also detailed in Appendix A.
Kt = Pt|t−1HT
t (HtPt|t−1HT
t + Rt)−1
(3)
ˆXt|t = ˆXt|t−1 + Kt(yt − Ht
ˆXt|t−1) (4)
Pt|t = (I − KtHt)Pt|t−1 (5)
Some rain fading measurements [14]–[17], show that the
attenuation has a linear behavior, and that the predicted values
will be updated by an additional amount at a certain rate.
Details about parameter values used are described in the
Results section. When the system starts, it computes the 1-
step ahead predictions based on the first N = 10 input
measurements and computes the mean for this window. Next,
like a moving-average, a new mean is generated using a new
measurement value. Finally, the difference between the current
and the last mean results in the slope. The value of the k-steps
ahead is multiplied by the slope and added to the last predicted
value, as shown by Fig. 4.
Fig. 4. Prediction diagram block showing how the k-steps ahead values are
computed based on the past measurements using linear Kalman filters
Due to the lack of measurements data for analysis of
rain fading at Ka-band for GEO satellites and based on the
measurements graphs from literature [14]–[17], we emulated
the rain attenuation behavior in order to develop and test
our prediction algorithm. Later this same synthetic signal is
considered as the measured Eb/N0 at the receiver which
represents the rain attenuation behavior. This signal is 15
minutes long and the sampling frequency is 1 Hz.
C. Decision Logic
The decision logic on the receiver decides if a radio recon-
figuration is required or not based on the predicted Eb/N0
value expected to be measured k-steps ahead. When required,
it informs the transmitter about the new modulation scheme
to be used. The Eb/N0 threshold for a certain modulation
scheme is set based on the Eb/N0 value for the maximum
BER allowed according to the BER curve acquired during the
calibration phase. On the MATLAB simulation we account
for the delay so that the reconfiguration only occurs after the
total delay time. If a different decision is made during the
delay time interval it will not be considered.
III. RESULTS
The proposed system simulation was implemented in MAT-
LAB. As mentioned in Section II, the AWGN channel input
is the synthetic Eb/N0 signal, which is the same expected to
be measured at the receiver in a real-world implementation.
The number of symbols sent remained constant throughout
all the simulations, being 30, 000 symbols/sec. For simplicity,
we simulated the system for adaptation between uncoded 4-
QAM and 16-QAM. The BER threshold was set to 10−3
,
which resulted in an Eb/N0 trigger point at 11.6 dB in the
16-QAM curve and in 7.6 dB in the 4-QAM curve. Our
design considers link loss every time the Eb/N0 is lower than
7.6 dB, i.e., transmissions made with the BER higher than
the allowed represent a zero goodput for the customer. In
the future we expect to implement a control channel which
allows the receiver to continuously measure the attenuation
while shutting down the high rate transmitter on orbit or on
ground to save battery power.
The predictor values are initialized as follows: The initial
values of the state x, the state variances pv and pc, the process
variance q and the measurement noise R are unknown. We
chose x0 = 0. Since the attenuation value and the rate are
uncorrelated pc = 0 and pv = 10, 000 since it will be corrected
with time. And the value of q was varied from q = 1 (e.g.
assuming we have an inaccurate model) up to a low value as
q = 10−10
(e.g. assuming we have a very accurate model). The
noise was assumed to be R = 1. With the exception of the q
values, the noise in the added portion ∆x, the initial choice of
these does not have a considerable effect in the overall system
performance. The prediction window for the predictor was set
N = 10 and the prediction horizon to 5 steps ahead, which
represents 5 seconds ahead. The system’s delay was set to be
equal to the prediction horizon, i.e., 5 seconds.
Fig. 5 shows the synthetic attenuation signal representing
the Eb/N0 measured by the receiver during rain and the
predicted value plotted at the instant it was predicted to
happen. Fig. 6 shows a close up portion of Fig 5.
Fig. 5. Rain attenuation predicted values k-steps ahead using noisy measure-
ments. The true value can not be seen due to the high amount of samples
We first simulated the system without the prediction and
adaptive features during the emulated rain event, the same
shown in Fig. 5, and collected the BER values. Next, two
additional scenarios were simulated: With both prediction and
adaptive features on and with the predictor off and the adaptive
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4. Fig. 6. Close up of Fig. 5. It can be seen that the predicted value is close to
the true value to be measured k-steps in the future.
feature on. Table 1 summarizes the BER performance for these
four scenarios.
According to Table 1, we can see that the BER for 16-QAM
was higher than 4-QAM because before and after the link
outage the transmissions were made during high attenuation
levels. But the interesting part is when the adaptation and
prediction scheme were used the BER performance was closer
to that when using only 4-QAM but an additional of 19 M bits
were transmitted. This is due to the fact that the predictor could
“see” the increase of attenuation ahead of time and decreased
the data rate by switching to a more robust modulation scheme.
After the receiver report that the BER was above the required
threshold the transmitter started transmitting again according
to the instructions received from the receiver and improved
the data rate when the attenuation level allowed to do so.
Also we can see that when we used the adaptation without
the prediction the BER was high. This is due to the fact
that the modulation switching was being triggered at the
wrong time instants leading to a decrease on the system’s
overall performance. Fig. 7 shows the system performance for
the third scenario, showing the change in the data rates, or
goodput, for the customer over time. The instantaneous BER
is also shown, where we can see that during the majority of
transmission time the system tried to keep the BER below the
threshold of 10−3
.
TABLE I
BER FOR DIFFERENT SIMULATED SCENARIOS
Mod Scheme Total bits Error bits BER
4-QAM (No adap) 44, 160, 000 1, 452 3.2880 × 10−5
16-QAM (No adap) 88, 320, 000 194, 805 2.2 × 10−3
Adap on and Pred on 63, 480, 000 4, 254 6.7013 × 10−5
Adapt on and Pred off 88, 320, 000 194, 805 2.2 × 10−3
IV. CONCLUSION
This paper showed the performance of the rain attenuation
prediction for adaptive modulation schemes for GEO satel-
lites operating at Ka-band. We simulated the communication
system in MATLAB based on a synthetic attenuation mea-
surement signal and showed the performance improvement in
terms of “goodput” when compared with a system not using
adaptation or prediction. The next steps are: (i) To improve the
Fig. 7. Received data rate changes according to the predicted Eb/N0 based
on the maximum allowed BER
prediction filter by using real measurement data or synthetic
data from channel simulators using rain cell models, (ii) To
increase the number of different modulation schemes using
different coding rates in order to get the best amount of data
before the system reaches the outage limit. Additionally we
plan to study the impact of different ground node speeds on
the attenuation slope at Ka-band regarding the total delay.
V. APPENDIX
We can measure only the state variable xt, not its rate. But
we can estimate them [18], [19]. Assuming a discrete sampling
interval ∆t = 1, our state matrix ˆX is given by:
ˆXt = ˆxt
dˆxt
dt
T
. (6)
In discrete mode we have:
Ft = e
∆t
0 1
0 0
=
1 ∆t
0 1
. (7)
Our noisy measurement matrix consists only of the attenu-
ation, the rate will be found by the iterations of the filter:
H = 1 0 , (8)
and
yt = yt 0
T
. (9)
The variance matrix Q for the discrete case [11]:
Q =
∆t
0
e
0 1
0 0
τ
0 0
0 q
e
0 1
0 0
T
τ
dτ =



∆t3
q
4
∆t2
q
2
∆t2
q
2 ∆t q


 .
(10)
The covariance matrix Pt:
P =
pv pc
pc pv
. (11)
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