Channel Overlapping Between IMT-Advanced Users and Fixed Satellite Service

International Journal of Electrical, Electronics and Computers (EEC Journal) [Vol-2, Issue-3, May-Jun 2017]
https://dx.doi.org/10.24001/eec.2.3.2 ISSN: 2456-2319
www.eecjournal.com Page | 12
Channel Overlapping Between IMT-Advanced
Users and Fixed Satellite Service
Kasim Abduljabar Alsalem
Computer Science Department, Cihan University- Sulaimanyia, Iraq.
Abstract— The Novel technique to modulate the nulls in
the radiation pattern of IMT-Advanced base station (BS)
towards the fixed satellite service is (FSS) affirmed in this
paper. Designing a new algorithm to extract the nulls in
the forbidding area and other base on MUSIC algorithm
to estimate the direction of mobile user and control the
handover technique is our major concern. A scenario of
two mobile users (MS) moving around one FSS had been
exclusively studied and simulated. Calculating the shortest
separation distance after identifying the critical point and
compare the result with the recent recommendation had
shown how magnificent coexistence and spectrum sharing
we can get.
Keywords—Fixed Satellite, C Band, IMT Advanced.
I. INTRODUCTION
Wireless interference is the one of the main factors which
degrades the performance of all the wireless
communications devices like cell phones, laptops and
Bluetooth’s. So, there have been always a certain
challenges and few possible solutions. According to the
world radio communication 2007 (WRC-07) for ITU with
resolution 228 the frequency band 3400-4200MHz (C-
Band) is the most expected candidate for IMT-Advanced
systems, on the other hand we have around 160
geostationary satellites operating in the band 3400 - 4200
MHz. However, C-band used over 40 years for FSS
because of its low atmospheric absorption, highly reliable
space-to-earth communication, wide service coverage and
geographical areas with severe rain fade conditions. While,
IMT-Advanced it’s a combination of different access
technologies; complement each technology in an optimum
way and provide a common and flexible service platform
for different services and applications. In contrast, C-band
is also important for IMT-Advanced because it allows use
of smaller antenna for terminals and base stations,
favorable to implement multiple antenna techniques and
enabling high spectrum efficiency. Some techniques had
been used before to solve such interference like: from
2003 up to 2005 mitigation by shielding and filtering had
been used to prevent the harmful interference from FWA
towards FSS. From 2005 up to now different techniques
like: tilting angle, beamforming and opportunistic MU-
MIMO (multi user- multi input multi output) had been
used.
Only one-tap equalizers are required to detect the data
provided that channel state information (CSI) is available.
The CSI can be acquired by training (or pilots) based
methods [1][2]. The disadvantage is loss in spectrum
efficiency. However, The minimum number of pilots
typically increases as the channel length increases.
For the second-order statistic (SOS) blind channel
estimation methods [3], channel is assumed to be invariant
for a long period of time so that reliable signal statistics
can be estimated Differentially encoded OFDM [4] only
requires the channel to be static over two consecutive
OFDM blocks, but it incurs a 3 dB performance penalty in
SNR. Therefore, Utilization of OFDM technique is
expected to offer improved performance in combating
adverse frequency selective fading encountered in
wideband MIMO wireless systems [5].
Major impediment in MIMO based systems is the cost of
hardware, because multiple antenna elements and RF
chains are deployed at each terminal. Therefore, a
promising technique named antenna selection has been
proposed to reduce the hardware complexity (save on RF
chains) while retaining many diversity benefits [6]. It
employs a number of RF chains, each of which is switched
to serve multiple antennas [7]. In recent years, there has
been increasing interest in applying antenna subset
selection to MIMO systems over frequency selective
channels [8], [9]. Several algorithms have been developed
for selecting the optimal antenna subset. MU-MIMO aims
at improving the overall system throughput by supporting
multiple users in a cell over the same time-frequency
resource. If the users are sufficiently spatially separated,
MU-MIMO significantly outperforms Single-User (SU)
MIMO.
The need to separate co-channel signals by an antenna
array arises in many situations and in cellular
communications in particular. Separating signals from
different users by their spatial signatures can increase
significantly system capacity beyond what can be achieved
by temporal processing alone. The multiple exploitation of
the same resource can be done either explicitly by forming
directional beams to several users (in this case often
denoted as Space Division Multiple Access (SDMA), or

implicitly by utilizing the spatial diversity of the multipath
radio channel by appropriate precoding / beamforming
techniques at the transmitter (often denoted as spatial
multiplexing). Combinations of both approaches are
possible. In general, beamforming-based solutions are
advantageous in outdoor wide-area cellular environments
with usually low angular spread, whereas spatial
multiplexing performs better in pico-cellular or indoor
scenarios with rich multipath propagation. Extensive work
has been done on techniques for separating co-channel
signals as described in [5] and [6].
The overwhelming majority of this work assumes that the
signals are point sources having zero angular spread, and
that the propagation from transmitter to receiver occurs
along one path or a few distinct paths (multi path). A more
practical model used in the cellular communications
environment represents the propagation as a continuum of
different paths, where the signal arriving at the receiver
antenna can be characterized by a continuous spatial
distribution of energy (angular spread) around the nominal
direction of arrival. Relatively little work has been done on
co-channel signal separation for the case of distributed
signal sources. Previously [7] developed a performance
bound for specifically the isotropic scattering case. More
recently, [8] proposed a structured estimate of the array
response, which performed better in a distributed fading
environment to a system that used an unstructured estimate
[9]. looked at the effect that temporal and spatial
correlation has on the DOA estimation, and [10]
investigated the performance of the general class of semi-
blind subspace-based estimators, like those that we are
using here.
Therefore, in this paper we study the effect of angular
spread on the ability of an array to separate co-channel
signals. While, SDMA –UPC (unitary Pre-coded matrix) –
MU-MIMO can be adjusted with limited feedback and null
steering technique modeling to mitigate the interference
from IMT-Advanced to FSS receiver. In particular we use
a framework where an isotropic scattering environment
was assumed (essentially an angular spread of 180
degrees) which is characteristics of a pico cell
environment. Here the angular spread can take on any
value between zero (point source) and 180 degrees.
Calculating the shortest separation distance after
identifying the critical point for fixed services and
compare the result with the recent recommendation had
shown how magnificent coexistence and spectrum sharing
we can get. Finally we developed a mechanism for hand
over technique to avoid the interference.
II. ALGORITHM DESIGN
The basic concept of the algorithm is to form nulls in the
spatial spectrum that correspond to the direction angles of
the victim FSS ES.
In this paper, for convenience the term ’DOE’ denotes the
direction angles of the victim FSS ES. First, the
IMTAdvance BS has to obtain DOE information in order
to perform nullsteering. DOE information can be obtained
by adopting a popular spatial spectrum estimation
direction finding method which is MUSIC Algorithem in
our case. The main steps of the algorithm are outlined as
follows:
1- Estimating Beamforming Weights: The beamforming
weights that will cancel the direction of FSS-ES must be
estimated from noisy snapshot data. Below, samples at the
ith
antenna are specified by si, where:
si = ui + vi; i = 1; 2; 3
and pairing the samples from antenna i and m, where again
mi  we define a 12 vector  T
mimi sss ,,  .
Next, those weights are used to search for a target with
threshold detection in each bin of interest that is obscured
by the jammer (FSS-ES).
2- Weight Estimation Method: Having constructed our
system model such that the jammer powers (other mobile
users) for any particular snapshot are equal in magnitude,
the only quantities that need to be estimated are the phases
W1,2, W1,3, and W2,3.
The least complicated way to estimate these is to measure
the phase differences between the signals at the individual
antennas for a collection of N snapshots, take an average,
and then add  fo Wi,m



N
k
k
m
k
imi SS
N
W
1
, )()(
1

Which results in the vector estimate
T
mwj
mi
i
eW 




,
, ,1
2
1
In the weighted sum, the jammer’s power is negligible, but
the target’s directional phase information is distorted. For
this reason the phase information has to be recovered in a
novel manor. So, if a target is detected, its threshold is
recorded and we proceed to step 3, if no target is detected,
we start over with the next coherent processing interval
(CPI=P(pulses)/PRF(pulse repetition frequency)).
3- Signal Detection: The first step in searching for a target
that is masked by an interfering signal is to estimate the
beamforming weights. For this estimation to be accurate,
the snapshots should not contain the target signal. If these
snapshots are obtained improperly, the algorithm might be
trained to cancel the target in the adapted beams. For this
reason, a system’s designer should consider the ranges
and/or velocities at which a target of interest might be. If
the interference source is a jammer, jammer-plus-noise
snapshots can be reliably obtained from the furthest
unambiguous range cells. After the training snapshots are
collected, and the weights have been estimated, they will

be used to search for a target signal within the jammer
dominated received signal. Multiplying the weighting
vectors with their respective received signal vectors results
in the residue power detection variable
2
3
2
2
3
2
22
1 2
4
1
}{
4
1
}{     mm
mvvEpE
Therefore, if a target signal is not present in the received
signals and the weight vectors are normalized, then the
average residue power will be equal to the average noise
power in the channels. Weighted sums in which the
jammer has been canceled can be made from groups or
from individual cells over any map region of interest; map
can be created from values of p.
4- Relative phase information is estimated at three
antennas. We need to find  and  by assuming the
jammer signal is canceled and the noise negligible, we get
a set of three equations
)1(
2
)(
2,1
2,1 

jj
eer
)1(
2
)(
3,1
3,1 

jj
eer
)1(
2
)(
3,2
3,2 

jj
eer
Because the components of the weight vector are of equal
magnitude, r1,2 bisects the angle subtended by the target
phasors shown in Figure below The bisection allows us to
write an equation that only involves the unknown phases.
)(2 2,12,1  r
 22 2,12,11  r
Note that the quantity 1 is known, whereas the quantities
on the right most side of the above equation are unknowns.
The final two weighted sum equations for r1,3 and r2,3 can
also be reduced to relationships involving only the phase
angles. These operations respectively yield:
;2;2 32  
Finally, estimating  and  is accomplished from
1323 ;  
5- Compute the nulls 0 generated by nT precoding vector
)1,...,1,0(,  Tmg nme .
6- Calculate nT precoding vector Wg,m (m=0,1,…,nT -1)
depending on DOE

 and the null 0 .
7- Select the nT-1 precoding vectors, Wg,n (n=0,1,…,nT-2),
forming nulls at DOE

 from nT precoding vectors Wg,m.
At the unitary precoding matrix U={E0,…,EG-1}, where
Eg=[eg,0,…,eg,nT-1] is the gth
precoding matrix. Eg,m is the
mth
precoding vector in the matrix Eg.
T
m
G
g
n
n
jm
G
g
n
j
T
mg
T
TT
ee
n
e ]...1[
1 ))(1(
2
)(
2
,





If the plane wave attacked the array at angle  with a
respect to the array normal the array propagation vector for
a uniformly spaced linear array is defined by
T
d
nj
d
j T
eev ]...1[
sin)1(2sin2 





Where  is a wavelength and d is is the space
between the antennas array and we consider d= 0.5  .
So, array factor in term of vector inner product:





1
0
}sin)(
1
{2
,
1
)(
T
T
n
k
d
m
G
g
n
kj
T
T
mgm e
n
veF



0 Satisfying Fm( )=0 and means null generated
by their precoding matrices and we need the nulls in the
DOE. In the order of steering the null to

 , the array
factor Fm( )for the precoding vector eg,m have to be
shifted to  , that is:





1
0
)}sin()(
1
{2
1
)(
T
T
n
k
d
m
G
g
n
kj
T
m e
n
F



For  =

 can be like:













1
0
1cos
sin2
sincos2
}sin)(
1
{2
])(
[
1
)(
T
T
n
k
d
kj
d
kj
d
m
G
g
n
kj
T
m
e
e
e
n
F








So, We can say
Fm(

 - )=(eg,m s)Tv
Where denotes Hadamard (pointwise) product
and

















1cos
sin)1(2sincos)1(2
1cos
sin2sincos2
)(...
)(1










d
nj
d
nj
d
j
d
j
TT
ee
ee
s
Therefore, adapted precoding vectors Wg,m for forming the
nulls at

 can be calculated as:
Wg,m=eg,m s
Because the beams produced by nT precoding vectors Wg,m
are mutually orthogonal, only one of nT beams does In
conclusion, nT-1 precoding vectors Wg,n are used for data
transmission of IMT-Advanced service.

From the beam patterns formed by the precoding vectors
eg,m illustrated in Fig.1, where G= 2, g=1, and nT = 4, it is
found that the null points 0 are ±14.50
and ±48.60
. Fig.2
shows mutually orthogonal overlapped beams produced by
the precoding vectors Wg,m for

 = -4.50
and  = 100
.
III. RESULTS AND DISCUSSION
The proposed MUSIC algorithm first estimates a basis for
the noise subspace and then determines the peaks the
associated angles provide the DOA estimates. The
MATLAB code for the MUSIC algorithm is sampled by
creating an array of steering vectors corresponding to the
angles in the vector angles.
Figure1 shows an example set of beams generated by an L-
shaped array for the case when the algorithm cancels a
jamming signal from -10± AZ and 25± EL. Before the
adaptations, all of the magnitudes, or gains, would have
been equal to 1.
Fig1: Beamformer output /2,1r as a function of target
AZ when antenna 1 and 2 are separated by a half
wavelength, and the jamming signal is at -100
AZ.
The beamformers from 1 to 2 and from 3 to 1 will be
equivalent to that shown in Figure 1. The beamformer
from antenna 2 to 3 however differs significantly from the
L-shaped case. It is plotted in Figure 2.
Fig.2: Shape of adapted beam obtained from sensors 2 and
3 in a uniform linear array configuration. Jamming signal
at -100
AZ.
The over all MUSIC spectrum for two mobile user one of
them on 200 and the second on 800 is represented in figure
3
Figure 3: MUSIC spectrum for two mobile users
Based on the results of Simulation, we conclude that,
while being a powerful algorithm and useful for complex
multi-signal scenarios, MUSIC’s computational
complexity is unwarranted in the problem on which we are
focused.
IV. CONCLUSION
The technique shows the nulls in the radiation pattern of
IMT-Advanced base station (BS) towards the fixed
satellite service (FSS). Designing a new algorithm to
extract the nulls in the forbidding area and other base on
MUSIC algorithm to estimate the direction of mobile user
and control the handover technique had been improved and
analyzed, This paper had prove that smart antenna is the

best mitigation technique can be use to avoid wireless
interference issues.
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[2] R. Negi and J. Cioffi, “Pilot tone selection for channel
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[3] B. Muquet, M. de Courville, and P. Duhamel,
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