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Co-Channel Interference Cancellation at the User
Terminal in Multibeam Satellite Systems
G. Cocco†, M. Angelone¶and A.I. Perez-Neira1,2
†German Aerospace Center - DLR
Oberpfaffenhofen, D-82234, Wessling, Germany
¶European Space Agency - ESTEC, Noordwijk – The Netherlands
1Centre Tecnol`ogic de Telecomunicacions de Catalunya – CTTC
Parc Mediterrani de la Tecnologia, Av. Carl Friedrich Gauss 7 08860, Castelldefels – Spain
2Department of Signal Theory and Communications
Universitat Politecnica de Catalunya, Barcelona, Spain
giuseppe.cocco@dlr.es, martina.angelone@esa.int, ana.perez@cttc.es
Abstract—We study the applicability of soft interference can-
cellation in the forward link of commercial multibeam satellite
systems with focus on mobile terminals. We adopt a standard
currently used in commercial satellite systems as a reference.
The multibeam satellite antenna radiation diagram has been
generated using a physical optics reflector model, while state-of-
the art channel models have been used for the land mobile satel-
lite (LMS) channel. The interference pattern has been derived
through a system simulator developed by the European Space
Agency (ESA). Starting from the analysis of the interference
pattern we study the application of a low complexity soft
interference cancellation scheme. Our results show that, under
realistic interference and propagation conditions and for existing
standards, a two-colors frequency reuse scheme can be employed
while guaranteeing service availability across the coverage and
keeping the complexity at the user terminals relatively low.
I. INTRODUCTION
Bandwidth limitation is one of the main capacity-limiting
factors in wireless communications systems. A common prac-
tice to overcome bandwidth shortage in both satellite and
terrestrial networks using multiple beams/cells consists in
dividing the available spectrum into sub-bands (colors) and
reuse them over non-adjacent geographical regions. However,
if on the one hand the reuse of frequency allows for a more
efficient utilization of the spectral resources, on the other hand
it determines an increase of the co-channel interference (CCI)
due to the non-ideal antennas radiation patterns. Despite the
improvements in antennas technology, undesired side lobes
still constitute challenging problem in geostationary (GEO)
satellite communications since the interference coming from
co-channel beams can heavily affect the reception of the
desired signal at the user terminal such that either the link
throughput or the availability are penalized. This problem is
exacerbated by the use of aggressive frequency reuse patterns.
Interference cancellation techniques at the user terminal (UT)
represent a possible solution to this problem. Many different
interference cancellation techniques have been proposed up to
date. A comprehensive overview is presented in [1]. A low
complexity option for mitigating interference is to adopt a
maximum a posteriori (MAP) symbol detector. Such detector
has the drawback of having a complexity that grows expo-
nentially with the number of signals to detect. In order to
keep complexity low, trading part of the performance, several
simplified schemes have been proposed in literature such as
[2], [3] and [4]. Iterative decoding has been shown to achieve
the multiple access channel (MAC) capacity in [5], by inte-
grating error control coding with multiple access interference
suppression. In [6] two iterative low complexity algorithms
for adjacent channel interference (ACI) cancellation in satellite
systems are presented. In [7] the authors proposed a parallel
multi-user detector for adjacent channel interference cancella-
tion in the return link of Inmarsat’s Broadband Global Area
Network (BGAN) system.
In the present paper we study the applicability of soft
co-channel interference cancellation in the forward link of
a satellite system with high frequency reuse based on a
realistic scenario with focus on mobile terminals. The results
presented here have been developed within the Advanced
Research in Telecommunications Systems (ARTES) [8] project
Next Generation Waveform for Increased Spectral Efficiency
(NGWISE) founded by the European Space Agency (ESA) [9].
The standard adopted in the European Telecommunications
Standard Institute (ETSI) Satellite Component of UMTS (S-
UMTS) [10] has been used as a baseline. Such standard is
currently used in commercial satellite systems [11]. The multi-
beam satellite antenna radiation diagram has been generated
through a commercial software used for satellite antenna de-
sign and analysis, while the interference pattern has been cal-
culated using a system simulator developed by ESA. A state-
of-the art channel model has been adopted for the land mobile
satellite (LMS) channel. Unlike most of previous works, we
start from the analysis of the interference distribution across
the coverage area. Based on the interference distribution we
propose a low complexity interference management solution.
It is worth noting that our work differs from [6] and [7]
in that co-channel rather than adjacent channel interference
is considered. In fact, whenever standard channel spacing is
considered 1and an aggressive frequency reuse scheme is
1this may not be the case if techniques such as time-frequency packing are
applied [12]. However, this falls out of the scope of the present work.
applied, indeed CCI becomes the most relevant source of
interference in the system as its level is much higher with
respect to the ACI. Unlike in [7] we consider the forward
link rather than the return link. Interference cancellation in
the forward link is constrained by the complexity at the UT,
especially within the LMS context. We show that, assuming a
realistic interference spatial distribution, the optimal detector
can be applied at the receiver with affordable complexity if
an appropriate synchronization of the signals at the feeder
stations and at the satellite is provided. Our results show that
frame error rates as low as 10−3can be achieved in the whole
covered area while using a two-color frequency reuse scheme
with a dramatic increase in spectral efficiency with respect
to coloring schemes usually adopted in commercial satellite
systems.
The rest of the paper is organized as follows. In Section II
the system model is presented while in Section III we describe
the proposed solution specifying the required modifications to
ETSI standard [13]. The numerical results are presented in
Section IV while Section V summarizes the main contributions
of the paper.
II. SYSTEM MODEL
Let us consider the forward link of an interactive geosta-
tionary (GEO) multibeam satellite system with 210 user-link
beams operating in L/S band. Each beam occupies half of the
available user-link bandwidth and a two-color frequency reuse
pattern is adopted, with a single polarization per beam.
Due to the satellite antenna radiation pattern each beam
suffers from the interference generated by the closest co-
channel beams. For a frequency-reuse of two, ten co-channel
beams can be identified as the most significant contributors to
the total interference. The geographical position of reference
and interfering beams is shown in Figure 1, where the red
color identifies the reference beam while orange is used for
the co-channel interfering beams. In order to be representative
of the best case and the worst case scenarios we considered
two beams for our analysis, namely beam 105 and beam 110,
one at the center and one at the edge of the global coverage,
respectively.
Given a UT in a given beam we refer to the desired signal
as reference signal. Single antenna terminals are considered.
The received signal at time twhen Nint interferers are
present is:
y(t) = h(t)gC(t)xC(t)
+
Nint
X
nint=1
gI
nint (tnint )xI
nint (tnint )#+n(t),(1)
where tnint =t−τnint ,τnint being the time offset of interferer
number nint, while
gI
nint (t) = GI
nint ej(2π∆νnint t+ϕnint ),(2)
GI
nint being the antenna gain of the co-channel interfering
beam nint in the direction of the UT, normalized to the gain of
the reference signal, while ∆νnint and ϕnint are the frequency
Fig. 1. Considered reference and interfering beams and conventional
numbering. Reference and interfering beams are shown in red and yellow,
respectively.
and phase offsets with respect to the local oscillator at the UT,
respectively. Similarly we defined
gC(t) = GCej(2π∆νCt+ϕC),(3)
with GC= 1.
Signals xC(t)and xI
nint (t),nint ∈ {1,...,Nint}, are
the reference and the interfering signals, respectively. The
interfering signals (and similarly the desired one) can be
expressed as
xI
nint (t) =
NCW
Nint
X
l=1
snint (l)g(t−lT nint
s),(4)
where g(t)is a root-raised cosine pulse with roll-off α,snint (l)
represents the l−th received symbol from interferer nint ,
Tnint
sis the symbol duration while NC W
nint is the number of
modulated symbols in a codeword for interferer nint .h(t)
takes into account the channel effect (phase rotation and prop-
agation loss). Note that h(t)is a common multiplying factor
for all signals, since all waveforms originate from the same
spacecraft and in a forward link system all signals cover the
same path to the UT. We assume that the maximum frequency
offset is such that ∆νnint TS≪1/100,∀nint ∈ {1,...,Nint}.
The sample taken at time tkafter matched filtering and
sampling of signal y(t)is:
yk=h(tk)gC(tk)s(k)
+
Nint
X
nint=1
gI
nint (tnint
k)
NCW
nint
X
l=1
snint (l)g(tnint
k−lT nint
s)
+wk,
(5)
where tnint
k=tk−τnint while wk’s are independently and
identically distributed (i.i.d.) zero mean complex Gaussian
random variables with variance σ2in each component. The
interfering signals gains GI
nint are determined by the satellite
antenna radiation pattern. The use of a realistic antenna pattern
is of fundamental importance for the selection and the per-
formance assessment of an adequate interference cancellation
technique at the UT. In the following we give details about
the antenna pattern and system model adopted in the present
paper.
A. System Simulations and Antenna Pattern Models
This section describes the system simulator developed by
ESA and used to compute the interference pattern as well as
the models used to create the considered antenna pattern.
The ESA satellite communication systems analysis tool,
developed in MATLAB, performs a multi-dimensional space-
time link budget over a uniform latitude-longitude grid of
users, averaging over a user-defined set of time availabilities
with the related channel attenuations and availability proba-
bilities. The reference propagation models are based on ITU
recommendation [14] and it is assumed that the traffic request
across different beams is uniform. For the sake of this study we
focus on clear sky conditions, since atmospheric attenuation
does not represent a serious impairment in L/S band. Each
user of the grid is assigned to a specific beam if the gain of
such beam in its location is the highest across the coverage.
Then, based on the frequency plan and on the consequent beam
coloring, the resulting interference pattern and distribution are
calculated. The simulated system foresees the use of Adaptive
Coding and Modulation (ACM) that enables each user to select
the most efficient modulation and coding (ModCod) scheme
allowed by the link condition. In general the ACM in LMS
systems is more challenging with respect to the case of fixed
terminals due to the rapid changes in the communication
channel induced by the terminal motion. In [13] a return
channel is used to feed-back the measured SNR (or SINR)
to the Bearer Control Layer. The information is used at the
control unit to select the bearer according to a target QoS.
Such system is used to adapt the communication rate to the
long-term channel variations only, since short-term fading is
covered by the link margin [13, Section 7]. Further analysis
in the implementation of the ACM mechanism is out of the
scope of this paper.
The down-link signal to interference ratio in the point x
belonging to beam iis given by:
C
IDL
co
(x) = PT X SAT (i)Gsat
T Xco−po (i, x)
PNco−ch
j=1 PT X SAT (j)Gsat
T Xco−po (j, x),(6)
where:
•PT X SAT (i)is the saturated power per carrier of beam i
•Gsat
T Xco−po (i, x)is the co-polar satellite TX antenna gain
of beam i in the location x
•PT X SAT (j)is the saturated power per carrier of beam
j; note that in the analysis it has been assumed that all
the carriers have equal power and therefore this term can
be assumed to be a constant
•Gsat
T Xco−po (j, x)is the co-polar satellite TX antenna gain
of co-channel beam beam jin the location x.
We assume that solid state power amplifiers (SSPAs) are used
on-board the satellite payload. As in this analysis we focus
on the first Nco = 10 strongest interferers received at the user
terminal we further define for each of them:
C
Ijco
(x) = Gsat
T Xco−po (i, x)
Gsat
T Xco−po (j, x),(7)
as the signal to co-channel interference related to the j-th co-
channel interferer, assuming that Ij≥Ij+1 ∀j∈ {1,...,Nco}
and INco+1 = 0. As for the considered antenna pattern, a
commercial software for antenna design analysis and coverage
planning has been used to reproduce a beam pattern similar
to the one of the commercial system BGAN [11], which
adopts the ETSI standard [13]. The software is based on
physical optics reflector modeling and allows for accurate
characterization of the directivity of both the co-polar and the
cross-polar fields, as well as scan-aberrations and losses [15].
A geostationary satellite in the 25 deg East orbital position
has been considered.
Finally, the reflector has been modeled with the parameters
listed in Table I.
III. PROPOSED SOLUTION
We consider the forward bearers family of the standard [13].
We aim to find an interference cancellation solution at the UT
that is at the same time efficient and that has low complexity.
We propose to split the complexity between system and UT
levels. In the following the modifications required at system
level with respect to the standard are detailed.
TABLE I
SATELLI TE REFLECTOR PARAMETERS.
Parameter Value
Aperture size [m] 9
F/D 1.34
Beam spacing/θ3dB [deg] 1.363
Crossover Level [dB] -3
Aperture Efficiency 59.1%
Directivity [dBi] 40.85
A. System Level
The modifications that would be required to [13] are here-
after specified and the related implications and feasibility
discussed.
1) It is assumed that the symbol rate RI
s= 1/T I
sof the
strongest interferer is the same as that of the reference
signal RC
s= 1/Ts,Tsbeing the symbol period of the
reference signal. Although in principle different channel
code rates, modulations and FEC block sizes may be
used in the two signals, the simulation results we present
in Section IV show that there are some restrictions to the
modulations and code rates that can be adopted. Note
that assuming the same symbol rate for the reference
and the interfering signals implies Tnint
s=Ts,∀nint ∈
{1,...,Nint}, in expression (5).
2) The symbols of reference and interfering signals are
aligned such that the intersymbol interference (ISI)-free
sample instants of the reference signal correspond to the
ISI-free sample instants of the interferer, which implies
τnint = 0 ∀nint ∈ {1,...,Nint}in expression (5).
However, in Section IV we show that this constraint can
be relaxed up to a certain extent.
3) The receiver knows the modulation used by the inter-
ferer. This information can be made available to the UT
through the global beam and using knowledge of the
user position, which is currently foreseen in [13] through
GPS signal. Knowing the position with respect to the ref-
erence beam, a user could derive which is the strongest
interfearing beam. The information about the modulation
used in each beam (and thus also in the interfearing
beam) during a given time slot is transmitted over the
global beam. We use this assumption as it simplifies the
description of the proposed scheme, although in Section
IV we will show that it can be actually removed.
B. MUD at the User Terminal
We assume that the channel of both the reference signal and
the strongest interferers as well as the ISI-free sample instants
of the reference signal can be estimated. This assumption is
usually taken in most multi-user detection (MUD) systems.
Channel estimation can be performed using the pilot symbols
inserted at regular intervals in the frame as foreseen in [13].
In case the pilot symbols of reference and interfering signal
overlap, joint estimation methods may be adopted (e.g., E-M
algorithm [16])2. An extensive literature is available on the
subject and further analysis is out of the scope of this paper.
We further assume that conditions 1→3of Section III-A
hold.
In case no interference is present, in a typical receiver
the turbo decoder is fed with the log-likelihood ratio (LLR)
vector of the sampled received signal. The j-th component,
j∈ {1,...,RNcw
C}of the LLR vector for QPSK signalling
2A similar problem has been addressed in [17] where the feasibility of the
joint estimation of phase, amplitude and frequency offsets of five colliding
signals is studied.
and using the Grey mapping scheme of [10] can be expressed
as:
LLRj= log P r{bj= 1 |yk}
P r{bj= 0 |yk}= log Pk,s2+Pk,s3
Pk,s0+Pk,s1,(8)
for j= 2k−1, while
LLRj= log Pk,s1+Pk,s3
Pk,s0+Pk,s2,(9)
for j= 2k, where Pk,snis the probability to observe the
sample ykconditioned to the transmission of the symbol sn,
n∈ {0,1,2,3}, while bjindicates the j-th coded bit in the
transmitted codeword. Equation (8), and similarly Equation
(9), is derived taking into account that, according to the
considered mapping, symbols s2and s3correspond to a bit
pair with the first bit equal to 1, while the first bit of the pair
mapping to s0and s1is equal to 0. Equation 8 can be easily
extended to the case of 16 QAM modulation. The probability
Pksnis proportional to:
Pk,sn∝exp |yk−h(tk)GCsn|2
2σ2.(10)
In the case of a single interferer with constellation size M,
the probability that the k-th symbol of the reference signal
s(k)is equal to sncan be expressed as:
Pk,sn=
M−1
X
m=0
PsI
mPk,sn,sI
m,(11)
where Pk,sn,sI
mrepresents the probability to receive ykcon-
ditioned to symbols snand sI
min the reference and in
the interfering signals, respectively, while PsI
mrepresents the
probability of transmitting symbol sI
m, which is assumed to
be equal to 1/M. The probability Pk,sn,sI
mis proportional to:
Pk,sn,sI
m∝exp |yk−h(tk)gC(tk)sn−h(tk)gI
nint (tk)sI
m|2
2σ2.(12)
This can be easily extended to the case of a generic number of
interferers Nint each with its phase and frequency offsets and
amplitude, leading to the following expression for the optimal
symbol detector,
Pk,sn=
M1−1
X
m1=0
···
MNint −1
X
mNint =0
Nint
Y
j=1
PsI
jPk,sn,sI
m1,...,sI
mNint
,(13)
where Mnint is the constellation size of interferer number
nint. The complexity of expression (13) grows exponentially
with the number of interferers.
Once the a-priori probabilities for the desired signal have
been derived they can be used to calculate the L-values to be
fed to the turbo decoder. In this case the only modification
at the receiver side with respect to the standard terminal is
limited to the signal detector, while no modification would
be needed at the decoder. The performance of the receiver
in terms of FER (and potentially in terms of throughput, as
higher ModCods could be adopted) can be further improved
through an iterative detection-decoding scheme, which would,
on the other hand, increase the receiver complexity and require
further modifications to the existing structure of the receiver
described in [10]. Such possibility is not further discussed here
for a matter of space.
IV. NUMERICAL RESULTS
In the following we evaluate the performance of the pro-
posed algorithm for the scenario described in Section II.
We start by describing in detail the reference scenario and
the interference distribution generated through the system level
simulator presented in SectionII. The beam numbering and
geographical location are those shown in Fig. 1. In Table II,
at the bottom of the page, we show the C/I related to each
of the 10 strongest interferers for both beam 105 and 110 in
two points, namely at the center of the beam (CoB) and at the
edge of the beam (EoB).
Fig. 2. Interference pattern and conventional numbering of the co-channel
beams. The central red rectangle represents the reference beam, while the
yellow rectangles represent the ten strongest interfering beams.
The relative numbering of the interferers is given according
to Figure 2, where the central rectangle represents the refer-
ence beam while the yellow rectangles represent the strongest
co-channel interferers.
With reference to Table II at the bottom of next page, it
can be seen that in the EoB cases the power of the interferer
number 5is comparable to that of the reference signal while
the second strongest interferer is attenuated more than 11
dB. On the other hand, in the CoB the strongest interferer
is at least 12 dB lower than the reference signal. Let us
consider the worst case scenario, i.e., the EoB. In this case
there is only one strong interferer plus nine interferers with
a relatively weak power, that, by the Central Limit Theorem,
can be modeled as Gaussian noise. Trying to apply MUD to
these low-power interferers is not likely to have a relevant
impact on the system performance while it would increase
significantly the complexity of the receiver. A better choice
is to apply the MUD to the desired signal and the strongest
interferer while treating the rest of the interferers as noise. In
order to understand whether the assumption of having at most
one significant interferer is realistic in each point of the beam
footprint, we analyzed the distribution of the total C/I across
the whole beam. The distribution is shown in Fig. 3, where
three cases have been considered for each point in the two
beams: i) all the interferers are present (top-left), ii) only the
first strongest interferer has been removed (bottom-left) iii)
the first two strongest interferers have been removed (bottom-
right). From the figure it can be seen that the total C/I reaches
negative values, in logarithmic scale, in some areas of the
beam (e.g., in the EoB points considered in the table shown
Fig. 3. Probability density function (PDF) of interference across the
covered area (horizontal axis represents C/I , expressed in dB) in case: 1) all
interferers are present (top-left), 2) the strongest interferer has been removed
(bottom-left), 3) the two strongest interferers have been removed (bottom-
right). The cumulative distribution function (CDF) of the difference between
the two strongest interferers across the beam is also shown (top-right).
in table II) when all the interferers are present. Removing the
strongest interferer determines a minimum C/I larger than
or equal to 6dB in any point of the two considered beams.
We further notice that the cancellation of the second strongest
interferer further increases the minimum C/I of only about
1-1.5dB. From the analysis of Fig. 3 we conclude that the
total C/I is mainly limited by the first strongest interferer while
the second one has only limited impact on performance. We
propose therefore to deal with only one interfering signal while
treating the others as noise in order to keep the complexity
low. The detector described in Section III. We recall that this
implies that the only modification needed at the decoder side
is in the detector, for which the a-priori probability in case of
one interferer reduces to:
Pk,sn=
M−1
X
m=0
exp |yk−hgC(tk)sn−hgI(tk)sI
m|2
2σ2
eq ,(14)
Mbeing the cardinality of the interferer’s constellation. The
correspondent block scheme is shown in Fig. 4.
In order to take into account the influence of the other
interferers (which reduces the reliability of the detection) in
the received signal’s statistics we increase σ2
eq by several
dBs (6in the following simulations) with respect to the
actual variance of the thermal noise σ2. The optimal choice
would be to choose the value of σeq by estimating the noise-
plus-interference power. However, in practice keeping a fixed
value of the variance can be a good compromise since i) the
thermal noise component can be either given by the terminal
manufacturer or easily estimated, while the power due to
residual interference may not be easy to measure, as the
Fig. 4. Proposed SIC scheme. Only the strongest interferer is taken into
account and no iterative detection is applied (i.e., reference signal is detected
as described in Section III and decoded using the BGAN turbo decoder).
Optionally also the strongest interfering signal can be decoded.
received signal is made up by the sum of the (strong) reference
signal, a (possibly strong) dominant interferer and the residual
interferer (estimation of the residual interference power in
such conditions would increase the complexity of the receiver)
and ii) we observed that the FER shows little sensibility to
the exact value of σeq . In the simulations presented in the
following the signal model described in equations 1-5 has
been adopted: all 10 interferers have been simulated including
channel code, modulation and channel effect, and scaling the
powers according to Table II. We first present the results
obtained in AWGN channel and then those for the LMS
scenario. The simulation setup for the two cases is depicted
in Fig. 5. The simplified scheme shown in Fig. 4 (i.e., the
received signal passes through the detector and through the
turbo decoder just once) has been used.
A. AWGN Channel
In figures 6, 7 and 8 we show the FER curves for the
considered MUD using the interference pattern detailed in
Table II. Different combinations of MODECODs available in
the standard [13] have been used, namely QPSK rate 1/3for
all signals in Fig. 6, QPSK with rate 2/5 for all signals in Fig.
7 and QPSK with rate 1/3for the reference signal and 16
QAM rate 1/3for interferers in Fig. 8.
From the plots it emerges that the target FER of 10−3can
be achieved using QPSK modulation in all signals up to rate
2/5while if 16 QAM is used in one of (or both) the signals the
target FER cannot be achieved for values of C/N of practical
interest.
In the following subsection we present the results for an
LMS scenario.
Fig. 5. Simulation setup in AWGN and LMS channels.
4 5 6 7 8 9 10 11 12 13 14 15
10−3
10−2
10−1
100
C/N (dB)
FER
Reference signal: bearer F80T1Q4B−L8, rate=0.3375, QPSK
Interferer: bearer F80T1Q4B−L8, rate=0.3375, QPSK
Fig. 6. FER in AWGN with MUD. Bearer F80T1Q4B-L8 (QPSK rate
1/3, symbol rate 33600 sym/sec, roll-off 0.25) is used for all signals. The
interference pattern for beam 110 EoB detailed in Table II (worst case
scenario) has been used.
B. LMS Channel
The channel model used in the simulations presented in the
following is a land-mobile satellite (LMS) channel for vehicles
moving at a speed of 50 kmph in a suburban environment. A
channel realization of 30 minutes (25 km path at 50 kmph)
has been used, corresponding to about 2.7·104FEC blocks for
bearer F80T025Q1B-L8 (QPSK, rate 1/3, symbol rate 8400
symbols per second). The time series has been generated using
an LMS channel generator implementing the Perez-Fontan
model [19].
TABLE II
TABLE W ITH FOU R SAMPLES OF THE INTERF EREN CE PATTERN. EACH ROW CONTAINS THE C/I RELATED TO TH E TEN STRONGEST INTERFERING
SIGNALS FOR EITHER A CENTER-O F-BE AM (BEST LO CATIO N)POINT OR AN EDGE-OF-BE AM (WORST LOCATION)POI NT IN BEAMS 105 (BEST BEAM)AND
110 (WORST BEAM). THE TOTAL C/I IS ALSO REPORTED F OR EACH CASE [18].
C/I [dB] C/I Total [dB]
Beam 1 2 3 4 5 6 7 8 9 10
105 CoB 37.1107 21.5885 32.1618 37.2214 17.9294 14.5272 27.3961 31.9697 20.7406 29.2564 11.46467
EoB 15.6046 15.5337 29.8048 15.8007 0.3881 15.1211 21.4581 44.3936 38.1297 21.875 -0.17835
110 CoB 30.3903 19.541 32.9636 35.0503 13.7636 12.1374 21.4154 29.4771 18.9879 30.8531 8.607253
EoB 27.2207 29.9124 22.4402 17.9726 0.1185 11.5821 18.8873 14.2254 15.2343 27.9627 -0.6047
4 6 8 10 12 14 16
10−3
10−2
10−1
100
C/N (dB)
FER
Reference signal: bearer F80T1Q4B−L7, rate=0.4, QPSK
Interferer: bearer F80T1Q4B−L7, rate=0.4, QPSK
Fig. 7. FER in AWGN with MUD. Bearer F80T1Q4B-L7 (QPSK rate
2/5, symbol rate 33600 sym/sec, roll-off 0.25) is used for all signals. The
interference pattern for beam 110 EoB detailed in Table II (worst case
scenario) has been used.
2 4 6 8 10 12 14 16
10−3
10−2
10−1
100
C/N (dB)
FER
Reference signal: bearer F80T1Q4B−L8, rate=0.3375, QPSK
Interferer: bearer F80T1X4B−L3, rate=0.33437, 16 QAM
Fig. 8. FER in AWGN with MUD. Bearer F80T1Q4B-L8 (QPSK rate 1/3,
symbol rate 33600 sym/sec, roll-off 0.25) is used for the reference signal
while bearer F80T1X4B-L3 (16 QAM rate 1/3, symbol rate 33600 symbols
per second , roll-off 0.25) is used for the interferers. Note that rate 1/3is
the lowest code rate available in BGAN. The interference pattern for beam
110 EoB detailed in Table II (worst case scenario) has been used.
In Fig. 9 we show the frame error rate for the reference
signal using the proposed simplified SIC scheme. 10 interferers
have been considered using the C/I values in Table II. Bearer
F80T025Q1B-L8 (QPSK, rate 1/3) of standard [13] has been
adopted for all signals.
Fig. 9 shows that the SIC scheme reaches the target FER of
10−3in all the considered cases, showing a neat enhancement
with respect to the case in which no interference cancellation
is applied. Thus it can be seen that decoding is possible in
all considered points, while it is not feasible without the
MUD algorithm. A relatively high C/N is required in order
to fulfill FER requirements in EoB which is due partly to
the challenging propagation scenario. As a matter of facts
it can be seen in Fig. 9 that, even in case no interference
is present in the system, a C/N of about 14 dB is needed
to reach a target FER of 10−3. We also note that the FER
obtained in the LMS channel in case of no interference is
almost the same as that in CoB. This is because the total
0 5 10 15 20 25 30
10−3
10−2
10−1
100
C/N (dB)
FER
Beam 110 EoC no IC
Beam 110 EoC
Beam 105 EoC
Beam 110 CoC no IC
Beam 110 CoC
Beam 105 CoC
No interference
Fig. 9. Frame error rate for the reference signal using the simplified SIC
scheme with one iteration (one detection and one decoding iteration). A 30
minutes LMS channel series in suburban environment generated according
to [20] has been used. 10 interferers have been considered using the C/I
values in Table II. Bearer F80T025Q1B-L8 (QPSK, channel code rate 1/3,
symbol rate 8400 symbols per second) of standard [10] has been adopted for
all signals.
interference level in CoB is low enough to allow for correct
decoding even without SIC, which justifies the fact that the
same performance is achieved by the SIC and the no IC (no
interference cancellation) schemes.
An important outcome of the simulations is that the system
results to be interference-limited mainly in the EoB area,
while interference has little effect in the CoB area. We also
showed that dealing with a single interferer is enough to make
decoding possible. We emphasize that these results have been
achieved with a limited increase in the receiver complexity,
as only the demapper has been modified with respect to
the receiver described in [10]. The fact that a C/N larger
than 20 dB is needed in EoB could be addressed by using
a code with longer codewords (the turbo code of DVB-SH,
for instance, has codewords which are an order of magnitude
larger than those used in the simulations just presented), an
interleaver with an adequate depth or a combination if the
two, compatibly with memory and latency constraints in the
user terminals.
V. CONCLUSIONS
We studied the application of co-channel soft interference
cancellation in multibeam mobile satellite systems with a two-
colour frequency reuse scheme. We took the ETSI standard
[13], currently used in commercial satellite systems, as a ref-
erence and simulated the beam radiation and the interference
patterns using a realistic antenna model. The calculation of the
interference pattern has been carried using simulator developed
by ESA. Due to strong complexity limitations in mobile
terminals, we proposed to move part of the complexity to
the system level, by aligning signals transmitted over different
beams and adding specific signalling information in the global
beam. We started from the analysis of the interference levels
across the beams selecting two of them as best and worst case
scenarios. In order to keep the complexity at the receiver low,
we proposed a simplified scheme in which only the detector
is modified with respect to the standard [10]. Our results
showed that even under challenging propagation conditions
and with strong interference, the considered scheme leads to
interesting results, achieving a target FER of practical interest.
We showed that the proposed approach may constitute a
concrete possibility to live together high levels of interference
with a relatively limited increase in complexity at both system
and user level. This comes at the expense of a limitation in
the ModCods that can be used.
ACKNOWLEDGEMENTS
The present work has been carried out under the ARTES
1programme founded by the European Space Agency. The
view expressed herein can in no way be taken to reflect the
official opinion of the European Space Agency.
The research leading to these results has received funding
from the Spanish Ministry of Science and Innovation under
projects TEC2011-29006-C03-02 (GRE3N-LINK-MAC) and
the Catalan Government (2009SGR0891).
REFERENCES
[1] J. G. Andrews, “Interference cancellation for cellular systems: a con-
temporary overview,” IEEE Wireless Comm., vol. 12, no. 2, pp. 19–29,
2005.
[2] X. Wang and H. V. Poor, “Iterative (turbo) soft interference cancellation
and decoding for coded CDMA,” IEEE Transactions on Comm., vol. 47,
no. 7, pp. 1046–1061, 1999.
[3] M. Kopbayashi, J. Boutros, and G. Caire, “Successive interference
cancellation with SISO decoding and EM channel estimation,” IEEE
Journal on Selected Areas in Comm., vol. 19, no. 8, pp. 1450–1460,
2001.
[4] G. Colavolpe, D. Fertonani, and A. Piemontese, “SISO detection over
linear channels with linear complexity in the number of interferers,”
IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 8, pp.
1475–1485, 2011.
[5] C. Schlegel, “Achieving the multiple-access capacity of the AWGN
channel with iterative processing,” in AESS European Conference on
Satellite Telecommunications (ESTEL), Rome, Italy, Oct. 2012.
[6] B. F. Beidas, H. El-Gamal, and S. Kay, “Iterative interference can-
cellation for high spectral efficiency satellite communications,” IEEE
Transactions on Comm., vol. 50, no. 1, pp. 31–36, 2002.
[7] M. Moher, W. Zhang, P. Febvre, and J. Rivera-Castro, in Advanced
Satellite Multimedia Systems conf. (ASMS) and Signal Processing for
Space Communications workshop (SPSC).
[8] European Space Agency (ESA), “Advanced Research in Telecommuni-
cations Systems (ARTES),” http://telecom.esa.int/telecom/.
[9] European Space Agency, “Next generation waveform for improved
spectral efficiency,” http://telecom.esa.int/telecom/, 2013.
[10] European Telecommunications Standards Institute, “Satellite component
of UMTS (S-UMTS) family SL satellite radio interface; part 2: Physical
layer specifications; sub-part 1: Physical layer interface,” May 2012.
[11] Inmarsat, “Broadband Global Area Network (BGAN),”
http://www.inmarsat.com/services/bgan.
[12] A. Barbieri, D. Fertonani, and G. Colavolpe, “Time-frequency pack-
ing for linear modulations: spectral efficiency and practical detection
schemes,” IEEE Transactions on Communications, vol. 57, no. 10, pp.
2951–2959, 2009.
[13] European Telecommunications Standards Institute, “Satellite component
of UMTS (S-UMTS) family SL satellite radio interface; part 1: General
specifications; sub-part 3: Satellite radio interface overview,” May 2011.
[14] International Telecommunications Union - Radiocommunications Sector,
“Propagation data and prediction methods requred for the design of
Earth-space telecommunication systems: methods requred for the design
of Earth-space telecommunication systems,” Reccomendation ITU-R
P.618-10 (10/2009), Oct. 2009.
[15] Satsoft, http://www.satsoft.com/.
[16] M. Feder and E. Weinstein, “Parameter estimation of superimposed
signals using the EM algorithm,” IEEE Trans. on Acoustics, Speech
and Signal Processing, vol. 36, no. 4, pp. 477–489, Apr. 1988.
[17] G. Cocco, N. Alagha, C. Ibars, and S. Cioni, “Practical issues in multi-
user physical layer network coding,” in IEEE Advanced Satellite Mobile
Systems conf. (ASMS), Baiona, Spain, Sep. 2012.
[18] European Space Agency, “NGWISE project technical Annex B: Tech-
nologies, models and requirements for the work ofRG2 on MSS,” 2013.
[19] F. Perez-Fontan, M. A. Vazquez-Castro, S. Buonomo, J. P. Poiares-
Baptista, and B. Arbesser-Rastburg, “S-band LMS propagation channel
behaviour for different environments, degrees of shadowing and eleva-
tion angles,” IEEE Trans. on Broadcasting, vol. 44, no. 1, pp. 40–76,
Mar. 1998.
[20] F. Perez-Fontan, A. Mayo, D. Marote, R. Prieto-Cerdeira, P. Marino,
F. Machado, and N. Riera, “Review of generative models for the
narrowband land mobile satellite propagation channel,” Int’l Journal of
Satellite Comm. and Networking, vol. 26, pp. 291–316, 2008.
