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Covariance Matrix Adjustment for Interference Cancellation Improvement in Adaptive Beamforming Chuwong Phongcharoenpanich,

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Covariance Matrix Adjustment for Interference Cancellation Improvement in Adaptive Beamforming Chuwong Phongcharoenpanich,
SUKHONTHAPHONG et al.:
COVARIANCE MATRIX ADJUSTMENT FOR INTERFERENCE CANCELLATION IMPROVEMENT …
27
Covariance Matrix Adjustment for Interference
Cancellation Improvement in Adaptive Beamforming
Thanakorn Sukhonthaphong, Phaisan Ngamjanyaporn, student member
Chuwong Phongcharoenpanich, member, and Monai Krairiksh, member
Faculty of Engineering and Research Center for Communications and Information Technology,
King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Email: [email protected]
ABSTRACT
This paper proposes the interference cancellation
improvement of smart antenna system by using
covariance matrix adjustment. This technique includes the
specific adjustable multipliers in both desired signal and
interference signal covariance matrices of complex
weight in order to overcome some disadvantages and
improve the interference cancellation efficiency in the
beamforming of adaptive array. The proposed
beamforming technique in this paper is based on the
complex weight which uses the covariance matrix for the
computation. The Applebaum array and the Linearly
Constrained Minimum-Variance (LCMV) method are
used in this paper. The simulation and experimental
results demonstrate that the proposed technique can
improve and increase the interference cancellation of
smart antenna to be more efficient than the conventional
technique.
Keywords: Adaptive array, LCMV, Applebaum array,
Beamforming, Covariance matrix, Interferencecancellation
1. INTRODUCTION
Smart antennas have recently received increasing a
role for using to improve the performance of wireless
communication systems [1]. These antenna systems are
composed of many components. The one important
component is a beamforming system that attempts to
enhance the desired signal and suppress the interference
signals. Among various beamforming schemes of smart
antenna systems, the Least Mean Square (LMS) array, the
Applebaum array algorithms proposed by Widrow, et al.
and Applebaum [2], respectively and the LCMV [3]
method, have been attracted considerable attention by a
large number of researchers. The LMS array uses the
comparison between the output and the reference signals
to pursue the minimization of the Mean Square Error
(MSE) whereas the Applebaum array endeavors to seek
the maximization of the desired signal-to-interferenceplus-thermal noise ratio (SINR). On the other hand,
LCMV method try to minimize the output power of the
CM5R16: Manuscript received on March 7, 2003; revised on
July 24, 2003.
adaptive array under constrain. The LMS array requires
the desired signal waveform, however, it does not need
incident angle knowledge. On the contrary, Applebaum
array and LCMV can be used when the incident angle of
the desired signal is known [2]-[6].
In practice, the convergent rate and the performance
of the system in adaptive array are important to perform
its usefulness. The Applebaum array has the advantages
in its simple hardware structure and fast convergent time.
However, the convergent time can be extremely long
when multiple interference signals impinge on the array,
causing an eigenvalue spread. Nevertheless, this
disadvantage can be overcome by using the GramSchmidt preprocessor, the Sample Matrix Inversion (SMI)
[4] and etc. These methods require large number of
computations and very complex hardware.
If the interference signals could be distinguished
according to their relative power level, the Applebaum
array and the LCMV can effectively remove all
interference signals regardless of the eigenvalue spread of
the input signal covariance matrix [4]. However, when the
received signal power level decreases and fluctuates, the
null response in the interference direction of beamforming
system will be disturbed and throb. Hence, the null
response direction is not exactly in the interference
direction, resulting in the performance degrading on
interference cancellation.
According to the aforementioned interference
cancellation disadvantage, this paper proposes the
covariance matrix adjustment technique to solve that
problem. The related work that uses the covariance matrix
to improve the performance of the system was shown in
[7]. However, the methodologies of these two techniques
are different. The work in [7] uses the spatial covariance
matrix transformation, while the proposed technique in
this paper includes the specific adjustable multipliers in
both desired signal and interference signal covariance
matrices of complex weight. This is to increase the low
power interference signals correlation and decrease the
correlation between the high powers of desired signal and
interfering signals. The proposed beamforming technique
is based on the complex weight which uses the covariance
matrix for the computation. Thus, the Applebaum array
and the LCMV are used in this proposed technique. This
will set exact deep null response in the interference signal
directions and high response in the desired signal
direction which can improve the weak signal problem.
28
ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS, VOL. 1, NO. 1, AUGUST 2003
= Φ d + Φi + Φ n .
This paper includes the conventional technique of the
Applebaum array and the LCMV method in section 2,
covariance matrix adjustment technique in section 3,
computer simulation results in section 4, experimentation
in section 5 and finally the conclusions in section 6.
θ
xN (t ) ..... x3 (t )
2. CONVENTIONAL TECHNIQUE
(7)
x2 (t )
x1 (t )
2.1 Applebaum Array
The concept of the Applebaum array begins with the
optimization criterion by considering the analytic signals
xi (t ) of an N-element adaptive array and complex
wN
w3
weights wi , i = 1, 2,..., N as shown in Fig.1, where θ is
the angle of arrival.
X = [ x1 (t ), x2 (t ),..., xN (t )]
T
(1)
(2)
They are multiplied by complex weights wi , then
summed to be the output signal s (t ) [2], [9]. It can be
expressed as
s (t ) = W T X = sd (t ) + si (t ) + sn (t )
(3)
with a vector form that under narrow-band uncorrelated
jamming sources assumption [2], [4].
The weight vector optimization in the Applebaum
array is based on maximization of SINR where
SINR =
Pd
Pd
=
.
Pu Pi + Pn
(4)
Pd, Pu, Pi and Pn are desired signal power, undesired
signal power, interference signal power and noise power,
respectively.
Pq =
2
1 ⎡
Ε ⎢ sq (t ) ⎤⎥ ,
⎦
2 ⎣
(5)
where q can be either d, u, i or n. At the steady state,
optimal weight vector of the Applebaum array converges
to Wiener-Hopf equation [4] that is given as
Wopt = µΦ −1U d*
(6)
where µ , U d and Φ represent an arbitrary constant,
desired signal and input covariance matrix, respectively.
Φ can be defined as
Φ =Ε( X * X T )=Ε( X d* X dT ) + Ε( X i* X iT ) + Ε( X n* X nT )
w1
Σ
s (t )
From Fig.1, the analytic signal xi (t ) [8], which may
consist of desired signal, interference and noise as
X = Xd + Xi + Xn .
w2
Fig.1: N-element Adaptive Array
2.2 LCMV Method
This approach is to minimize the mean square output
2
( E ⎡ S ⎤ ), subject to the following linear constraints: if
⎣ ⎦
the input X is a column of a constraint matrix C, then the
output must equal the corresponding element of a
specified gain vector f, the optimum weight [3] can be
shown as
Wopt = Φ −1C (C T Φ −1C ) −1 f .
(8)
From equation (6) and (8), the complex weights are
composed of the covariance matrix both the Applebaum
array and the LCMV methods by which their interference
signal power level is important for interference
cancellation beamforming. When the received Signal-toNoise-Ratio (SNR) level is diminished [4], the output
SINR pattern response of both techniques at the
interference direction will be affected. The output null
pattern response will not exactly be at the interference
direction. In this case, the interference cancellation
efficiency is decreased. Although, there are an arbitrary
constant µ , which can adjust weight vector in (6), of the
Applebaum array and gain vector f in (8) of the LCMV
method, they can not specify the multiplier in each
covariance matrix for both the interference signal
covariance matrix Φ i and the desired signal covariance
matrix Φ d , which have different Interference to Noise
Ratio (INR) and SNR, respectively. Thus, adjusting
arbitrary constant or gain vector is not the efficient
solution. For this reason, when the received SNR level is
small, the interference cancellation capability of the
conventional technique is decreased.
3. COVARIANCE MATRIX ADJUSTMENT
TECHNIQUE
Since
the
disadvantage
of
the
conventional
SUKHONTHAPHONG et al.:
COVARIANCE MATRIX ADJUSTMENT FOR INTERFERENCE CANCELLATION IMPROVEMENT … 29
techniques occurs when the SNR level of the received
signal decreases or the noise increases, we propose the
covariance matrix adjustment technique to improve that
mentioned disadvantage. The proposed covariance matrix
adjustment technique is the process that adds the
adjustable multipliers to both interference signal
covariance matrix Φ i and desired signal plus noise
covariance matrix Φ dn , in the complex weight analysis,
for controlling output null response in the interference
signal directions and output peak response in the desired
signal direction. However, there is the research that
relates to this technique, in which the spatial covariance
matrix transformation is used in the weight computation
[7]. The technique is similar to the proposed technique
presented in this paper, but the methodologies of both
methods are different. The spatial covariance matrix
transformation technique uses the inverse rectangular
window to approximate the interference covariance
matrix [7] which differs from the proposed covariance
matrix adjustment technique that uses the adjustable
multiplications of the interference covariance matrix by
considering the correlation between the received signals
to enforce the beam pattern of the receiving array
antenna. In addition, the proposed covariance matrix
adjustment technique in this paper uses the spatial
smoothing technique in the DOA estimation that can
predict the correlated signal impinging at the receiving
array antenna. In contrast, the spatial covariance matrix
transformation technique uses only Capon’s method.
In order to increase the low power interference
signals correlation, the adjustable multiplier with Φ i
should be high, meanwhile to decrease the correlation
between the high powers of desired signal and interfering
signals, the adjustable multiplier with Φ d should be low.
Thus, the proposed adjusted covariance matrix ( Φ adj ) is
defined as
Φ adj = BΦ dn + C Φ i ,
(9)
where B and C are the adjustable multipliers. The desired
signal plus noise covariance matrix consisting of both
desired signal term and noise term ( Φ dn = Φ d + Φ n ), is
multiplied by B. Meanwhile the interference covariance
matrix is multiplied by C when there is the interference.
Therefore B has to be low value while C has to be high
value which the value of B and C depend on the value of
input SNR and input INR, respectively. If there are the
interference signals from many directions, the
interference covariance matrix will consist of many
interference covariance matrices. In this case, the
directions of the desired signal and the interference
signals are derived from the Direction Of Arrival (DOA)
estimations [10] (Capon’s minimum-variance method,
MUSIC and spatial smoothing technique [11]-[14]). For
example, if there are the interference from three
directions, Φ i will consist of Φ i1 , Φ i 2 and Φ i 3 . Hence,
C will consist of C1 , C2 and C3 that can be expressed as
Φ adj = BΦ dn + C1Φ i1 + C2 Φ i 2 + C3 Φ i 3 .
(10)
In this context, if each input INR of interference signal is
different, each Ci of interference covariance matrix will
not be identical. If each of the SNR and INR increases to
be larger than the upper threshold, the values of B and C
will be decreased or inversely proportional to their SNR
and INR values which relate to the previous revolutionary
sample, respectively. On the contrary, if each of the SNR
and INR decrease to be less than the lower threshold, the
value of B and C will be increased or inversely
proportional to their SNR and INR, respectively. Thus
B = Bref ×
Ad ref
C = Cref ×
Airef
(11)
Ad
and
Ain
.
(12)
Otherwise, if each of the SNR and INR are between
the upper and the lower thresholds, B and C will change
directly proportional to their SNR and INR values which
relates to the previous revolutionary sample.
B = Bref ×
Ad
Ad ref
(13)
C = Cref ×
Ain
.
Airef
(14)
and
Bref and Cref are the reference values of B and C,
respectively, which are set to be the constant values.
Ad ref and Airef are the reference values of the desired
signal and interfering signal amplitudes which can be
provided by the DOA estimation at the reference
situation. Ad and Ain are the amplitudes of the received
desired signal and the nth interfering signal, respectively.
They can be achieved by DOA estimation instantaneously
in the adaptive process.
The values of B and C are considered from the input
signal of the system. Thus, it is necessary to have the
control operation for comparing the input signal level and
its threshold. The values of B and C can be considered
from Fig.2 and Fig.3 which are the examples of relation
between the level of output SINR of the Applebaum array
and B for various C values in the 30o, 40 dB SNR desired
signal and 60o, 30 dB INR interference signal case. Fig.2
and Fig.3 illustrate that the output SINR in the desired
signal direction and the output SINR in the interference
direction are proportional and inversely proportional to
the value of B and C, respectively. To minimize output
SINR in the interference direction, and maximize the
output SINR in the desired direction simultaneously, B
30
ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS, VOL. 1, NO. 1, AUGUST 2003
of interference signals is more than the degree of
freedom, it is necessary to choose the suitable interference
from the consecutive high INR level.
After the adjusted covariance matrix is obtained, the
complex weight can be computed. We can then see that
the weight solution of the improved covariance matrix
adjustment technique for the Applebaum array and the
LCMV method will multiply with the input signal of each
array element and are summed to be an output signal.
Output SINR(dB) in Desired Direction
45.4774038
C=1
C=5
C=10
C=30
C=50
45.4774036
45.4774034
45.4774032
45.4774030
45.4774028
45.4774026
45.4774024
0.001
0.0015
0.002
0.0025
0.003
0.0035
0.004
B
-70
C=1
C=5
C=10
C=30
C=50
-80
-90
-100
-110
In this section, the interference cancellation
performance of the covariance matrix adjustment
technique applied for Applebaum array and the LCMV
method is clarified by simulation results of SINR pattern
response. It is found that null response can be improved in
the interference signal directions. To compare the
performance of the proposed covariance matrix
adjustment technique with the conventional technique, the
Applebaum array consisting of four isotropic elements
with half wavelength apart between elements is
considered.
-120
-130
20
0.0015
0.002
0.0025
B
0.003
0.0035
0.004
Fig.3: Comparison of Output SINR in Interference
Direction versus B for Various C Values of 30o 40 dB
SNR Desired Signal and 60o 30 dB INR
Interference Signal
and C can be set to 1/500 and 30, respectively, while SNR
value of the desired signal and INR value of the
interference signal are between 10 dB and 50 dB. These
B, C, SNR and INR can be set as the reference of the
system.
The operation of the covariance matrix adjustment
technique begins with DOA receives the input signal to
estimate the signal directions. The next process is the
decision part for considering the number of interference
signals. If there is no interference, the adjusted covariance
matrix will be set as desired signal covariance matrix,
then its determinant is checked. The covariance matrix
will be inverted ( Φ −1 ) in the complex weight
computation [2], thus the covariance matrix should be the
nonsingular matrix ( det(Φ ) ≠ 0 ). The important cause of
singular matrix problem is the signal reduction or the
weakness of the signal [4] that can be improved by
readjusting the covariance matrix to increase the values of
B and C. In addition, this technique has to include the
degree of freedom decision [2] to divide and choose the
interference in the suitable direction, because the number
of null response pattern directions that can be set for
interference cancellation of the linear array system is
equal to the degree of freedom (N-1), where N is the
number of the array elements. However, when the number
SINR Response Pattern (dB)
-140
0.001
Proposed Technique
Conventional Technique
0
-20
-40
-60
-80
-100
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90
Angle of Arrival (deg)
Fig.4: SINR Response Pattern of Four-Element
Applebaum Array with -10o, 40 dB SNR, Desired Signal
with Three Interference Signals at -70o, -50o
and 50o with the Same 30 dB INR
20
SINR Response Pattern (dB)
Output SINR(dB) in Interference Direction
Fig.2: Comparison of Output SINR in Desired Direction
versus B for Various C Values of 30o 40 dB SNR Desired
Signal and 60o 30 dB INR Interference Signal
4. COMPUTER SIMULATION RESULTS
Proposed Technique:condition1
Conventional Technique:condition1
Proposed Technique:condition2
Conventional Technique:condition2
0
-20
-40
-60
-80
-100
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90
Angle of Arrival (deg)
Fig.5: Comparison of SINR Response Pattern of FourElement Array between the Conventional Applebaum
Array and the Proposed Technique for Two Conditions
COVARIANCE MATRIX ADJUSTMENT FOR INTERFERENCE CANCELLATION IMPROVEMENT … 31
SINR Response Pattern (dB)
20
Proposed Technique:condition1
Conventional Technique:condition1
Proposed Technique:condition2
Conventional Technique:condition2
0
-20
-40
-60
-80
-100
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90
Angle of Arrival (deg)
Fig.6: Comparison of SINR Response Pattern of FiveElement Array between the Conventional Applebaum
Array and the Proposed Technique for Two Conditions
20
SINR Response Pattern (dB)
The maximum number of incident interference signals
that can be rejected is equal to the degree of freedom that
is N-1 or three incident interference signals. When there
are three interference signals with same INR of 30 dB
from incident angles of -70o, -50o and 50o with 40 dB
SNR desired signal from the incident angle of -10o, the
SINR response pattern of the conventional Applebaum
array and the proposed technique can be presented in
Fig.4. It represents more effective null response setting in
the interference signal directions of the proposed
technique than the conventional Applebaum array. In the
case that the received signal power decreases, the
response pattern of beamforming in adaptive array will be
affected as shown in Fig.5. Condition 1, the desired
signal is in -40o incident angle with 40 dB SNR and three
interference signals are in -70o, 20o and 60o with 10 dB,
10 dB and 15 dB INRs, respectively. The output SINR
response pattern in the interference directions of the
conventional Applebaum array fluctuates and is not
accurate, when the weak signal is experienced in the
conventional Applebaum array. It is found that the SINR
response pattern of the proposed covariance matrix
adjustment technique can still set exact null response
pattern in the interference directions. In case of condition
2, the desired signal comes from 20o with 40 dB SNR and
three interference signals come from -40o, 45o and 70o
with 10 dB, 10 dB, and 15 dB INRs, respectively. The
results of the proposed interference cancellation technique
are still more effective than the conventional Applebaum
array, that can be clarified. In addition, the number of the
array elements is an important factor that has a role to
increase the interference cancellation efficiency. Since the
number of array elements is increased, the degree of
freedom in this Applebaum array increases in the same
trend. This should increase interference cancellation
efficiency because it can reject more incident interference
signals that come from many directions than less array
element number. It can be shown in Fig.6, which
demonstrates that although the number of array elements
is increased the proposed Applebaum covariance matrix
adjustment technique can still set null response pattern in
the interference directions better than the conventional
Applebaum array. In Fig.6, condition 1, desired signal
comes from -40o with 40 dB SNR and four interference
signals come from -70o, 20o, 40o and 60o with 10 dB,
10 dB, 15 dB and 15 dB INRs, respectively. Condition 2,
40 dB SNR of desired signal comes from 20o and four
interference signals come from -60o, -40o, 45o and 70o
with 10 dB, 10 dB, 15 dB and 15 dB INRs, respectively.
By using eight-element array antenna with half
wavelength array element spacing, the simulation results
of the Applebaum array and the LCMV method can be
shown in Fig.7 and Fig.8, respectively. In this case, the
response pattern of the conventional technique can not set
null response pattern in all interference directions for both
condition 1 and condition 2 as defined in Fig.5, while the
proposed technique can set null response pattern exactly
Proposed Technique:condition1
Conventional Technique:condition1
Proposed Technique:condition2
Conventional Technique:condition2
0
-20
-40
-60
-80
-100
-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90
Angle of Arrival (deg)
Fig.7: Comparison of SINR Response Pattern of EightElement Array between the Conventional Applebaum
Array and the Proposed Technique for the Same
Conditions of Fig.5
20
SINR Response Pattern (dB)
SUKHONTHAPHONG et al.:
Proposed Technique:condition1
Conventional Technique:condition1
Proposed Technique:condition2
Conventional Technique:condition2
0
-20
-40
-60
-80
-100
-90 -80 -70 -60 -50 -40 -30 -20 -10 0
10 20 30 40 50 60 70 80 90
Angle of Arrival (deg)
Fig.8: Comparison of SINR Response Pattern of EightElement Array between the Conventional LCMV Method
and the Proposed Technique for the Same
Conditions of Fig.5
in the interference directions. The good response pattern
close to the desired direction is achieved.
5. EXPERIMENTATION
5.1 Experimental Configuration
The experiments were conducted in the anechoic
32
ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS, VOL. 1, NO. 1, AUGUST 2003
chamber of the Communications Research Laboratory
(CRL) Japan at Yokosuka Radio Communications
Research Center.
The positions of desired signal and interference
transmitters and receiver are depicted in Fig.9. The
desired transmitting antenna is a horn and the interfering
transmitting antenna is a single patch, while the received
antenna is an eight-element patch antenna array with half
wavelength inter-element. The π/4 QPSK modulated
signal is transmitted at the carrier frequency of 2.335
GHz. The sampling rate of the receiver is 1.8 MHz and IF
is 450 kHz. The block diagram of the receiver can be
shown in Fig.10.
All eight elements of the array antenna were
connected to down converters for converting the received
signal in each branch of array antenna down to 450 kHz
IF. The IF signal in each channel passed the A/D
converter for changing an analog signal to a digital signal
before adaptive processed by the computer. In adaptive
process, the signal from each branch was collected
separately among the other branches.
Receiver
-18.46°
10.70 m
10.80 m
79.13°
3.45 m
Horn Antenna
(Desired Signal)
Single Patch Antenna
(Interference)
Fig.9: Configuration of the Experiment (not to scale)
Antenna. #1
.
.
Antenna #8
Down
Convertor
Down
DownConvertor
Convertor
Down Converter
BPF
LNA
BPF
LNA
BPF
LNA 2.335GHz
to
450kHz
Oscillator
Oscillator2.335GHz
2.335GHz
Oscillator 2.335GHz
IF
450kHz
A/D
1.8MHz
Adaptive
process
Output Signal
Fig.10: Block Diagram of the Receiver
The collected data were used to estimate the direction
of incident signal and the relative power intensity by
using the Capon’s minimum-variance method [14] before
the complex weight computation. In this case, the relative
power intensities in the incident directions of the received
signals were used to provide covariance matrix for the
complex weight computation.
In order to realize the precise computing processes in
both of direction estimation and complex weight
computation, calibration procedure is indispensable to
compensate particular amplitude and phase imbalance
among RF circuits of the antenna branch [15]. Moreover,
the complex weights were computed in various situations
of the experiment with the same directions of the desired
and the interfering signals. The experimental results are
presented below.
5.2 Experimental Results
In this section, the output weight in various
experimental situations were computed and used to adjust
the beam pattern of the receiving array antenna by
multiplying the complex weight with the beam pattern of
the receiving antenna for realizing the interference
rejection capability. Since the Applebaum array needs the
knowledge of incident angle, the direction estimation
technique is required. Accordingly, to find the direction
and relative power intensity of the received signal used in
covariance matrix computation, the Capon’s minimumvariance method is applied for the Applebaum array. At
first, the radiation pattern of the receiving antenna before
complex weight adjusting was measured as shown in
Fig.11. In the adjusted case of two noncoherent
transmitters, the beam pattern of the receiving antenna
after adjusted or multiplied by complex weight of both the
conventional technique and the proposed technique are
compared in Fig.12(a). The direction of the desired signal
and the interfering signal are 0o and -18.46o, respectively.
The relative power in the desired signal and the
interference directions of the conventional technique are
-15.5 dB and -3 dB, respectively, but those of the
proposed technique are -5.5 dB and -15 dB, respectively.
In addition, in Fig.12(b), the direction of the desired
signal is 10o and the interfering signal direction is -8.46o.
The values of the beam pattern in the desired signal
direction and the interference signal direction of
the conventional technique are -7 dB and -10 dB,
respectively. On the other hand, those of the proposed
technique are -7 dB and -19 dB, respectively. It should be
noted that the beam pattern in the interfering direction of
the proposed technique is deeper and more exact than the
conventional technique, 12 dB in Fig.12(a) and 9 dB in
Fig.12(b). The beam pattern in the desired direction of the
proposed technique still has better response than that of
the conventional technique. Moreover, the proposed
technique can still be used with the LCMV method as
shown in Fig.13(a) and (b), which show that the proposed
technique provides the more effective in beamforming
than the conventional technique. In Fig.13(a), the relative
power in the desired signal direction and the interference
SUKHONTHAPHONG et al.:
COVARIANCE MATRIX ADJUSTMENT FOR INTERFERENCE CANCELLATION IMPROVEMENT … 33
0o
-30o
0 dB
0o
30o
-30o
-10 dB
-60o
60o
-20 dB
-30 dB
o
-90
90
-120o
120o
o
90o
-90
-120o
150o
120o
-150o
150o
180o
180o
Fig.11: Azimuth Pattern of Receiving Antenna before
Complex Weight Adjustment
Conventional Technique
0o
30o
-30o
-10 dB
60o
-20 dB
-60
-30 dB
90o
-120o
120o
150o
Proposed Technique
-120o
120o
150o
30o
-10 dB
-60o
60o
-20 dB
-30 dB
-90o
90o
-120o
Conventional Technique
Proposed Technique
(b)
Fig.13: Received Beam Patterns after Adaptation of
LCMV (a) 0o Desired Signal and -18.46o
Interference Directions, (b) 10o Desired Signal
and -8.46o Interference Directions.
0o
120o
150o
180o
Conventional Technique
90o
-90
180o
(a)
-150o
o
-150o
180o
0 dB
60o
-20 dB
-90o
-30o
30o
o
-30 dB
Conventional Technique
0 dB
-10 dB
-60o
-150o
Proposed Technique
(a)
0o
0 dB
60o
-20 dB
-30 dB
o
-30o
30o
-10 dB
-60o
-150o
0 dB
Proposed Technique
(b)
Fig.12: Received Beam Patterns after Adaptation of
Applebaum Array, (a) 0o Desired Signal and 18.46o Interference Directions, (b) 10o Desired
Signal and -8.46o Interference Directions.
signal direction of the conventional technique are -12.6
dB and -9 dB, respectively, whereas those of the proposed
technique are -5.5 dB and -15 dB, respectively. In
Fig.13(b), the relative power in the desired signal and the
interference directions of the conventional technique are 2.2 dB and -4.5 dB, respectively. On the contrary, those
of the proposed technique are -6.6 dB and -19.7 dB,
respectively. It can be concluded that the beam pattern in
the interfering direction of the proposed technique is
deeper and more exact than the conventional technique,
6 dB in Fig.13(a) and 15.2 dB in Fig.13(b). Furthermore,
when the interference is coherent signal, the beam
patterns of the received array antenna are presented and
compared for various power levels of the interference in
Fig.14. In this case, the DOA can be estimated from the
spatial smoothing technique with MUSIC method [13].
The power of the desired signal was fixed while the
34
ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS, VOL. 1, NO. 1, AUGUST 2003
power of the interference was varied 3 dB down per one
step from Exp.1 to Exp.5 by using attenuator. Therefore,
the output power of interference from Exp.1 to Exp.5 are
-38 dB, -41 dB, -44 dB, -47 dB and -50 dB, respectively.
The direction of the desired signal was 0o while
interfering signal was -18.46o. Also, in Fig.15 the beam
patterns of the receiving antenna were compared in the
same situation of Fig.14, but the directions of the desired
signal and the interfering signal were changed. In Fig. 15,
the direction of the desired signal was 10o while the
interfering signal direction was -8.46o. The average
powers of both conventional and proposed techniques in
the desired direction of Fig.14 are -6.3 dB and -3.9 dB,
respectively, whereas those of Fig.15 are -3.6 dB and -2.9
dB, respectively.
In the case of interfering direction of Fig.14, the
average power of both the conventional and the proposed
techniques are -10.0 dB and -15.1 dB, respectively, while
those of Fig.15 are -7.3 dB and -21.2 dB, respectively.
Therefore, the power level of the proposed technique in
the desired direction of Fig.14 is 2.4 dB higher than the
conventional technique, while Fig.15 is 0.7 dB higher
than the conventional technique.
Besides, the power level of the proposed technique in
the interfering direction of Fig.14 is 5.1 dB lower than the
conventional technique, while Fig.15 is 13.9 dB lower
than the conventional technique. Thus, the descriptive
results can verify the more effective of the proposed
technique that the beamforming can set null beam at the
interference direction and more effective response pattern
0o
-30o
0 dB
0o
30o
o
-30
0 dB
30o
-10 dB
-60o
-10 dB
60o
-20 dB
-60o
60o
-20 dB
-30 dB
-90o
90o
-30 dB
-90o
90o
120o
-120o
120o
-120o
-150o
150o
-150o
180o
150o
o
180
Exp.1
Exp.2
Exp.3
Exp.4
Exp.5
Exp.1
(a)
Exp.2
-30
Exp.4
Exp.5
(a)
0o
o
Exp.3
0 dB
0o
o
30
-30o
0 dB
30o
-10 dB
-60o
-10 dB
60o
-20 dB
-60o
60o
-20 dB
-30 dB
o
-30 dB
90o
-90
-90o
90o
120o
-120o
120o
-120o
-150o
150o
-150o
o
180
Exp.1
Exp.2
Exp.3
Exp.4
150o
180o
Exp.5
(b)
Fig.14: Applebaum Array Beam Pattern of Receiving
Antenna for Various Interference Power Levels
when Desired and Interference Directions are 0o
and -18.46o, respectively, (a) Conventional
Technique, (b) Proposed Technique.
Exp.1
Exp.2
Exp.3
Exp.4
Exp.5
(b)
Fig.15: Applebaum Array Beam Pattern of Receiving
Antenna for Various Interference Power Levels
when Desired and Interference Directions are 10o
and -8.46o, respectively, (a) Conventional
Technique, (b) Proposed Technique.
SUKHONTHAPHONG et al.:
COVARIANCE MATRIX ADJUSTMENT FOR INTERFERENCE CANCELLATION IMPROVEMENT … 35
0o
0o
-30o
0 dB
-30o
30o
-60o
60o
-20 dB
-30 dB
-90o
90o
120o
-120o
-90o
90o
120o
-120o
-150o
150o
150o
180o
180o
Exp.1
Exp.2
Exp.3
Exp.4
Exp.1
Exp.5
Exp.2
0 dB
-30o
30o
-60o
60o
-20 dB
90
o
120o
-120o
60o
o
90o
-90
120o
-120o
-150o
150o
150o
o
o
180
180
Exp.3
30o
-30 dB
-90
-150o
0 dB
-20 dB
-30 dB
o
Exp.2
Exp.5
-10 dB
-10 dB
Exp.1
Exp.4
0o
0o
-60o
Exp.3
(a)
(a)
-30o
60o
-20 dB
-30 dB
-150o
30o
-10 dB
-10 dB
-60o
0 dB
Exp.4
Exp.5
(b)
Fig.16: LCMV Beam Pattern of Receiving Antenna for
Various Interference Power Levels when Desired and
Interference Directions are 0o and -18.46o, respectively,
(a) Conventional Technique, (b) Proposed Technique.
in the desired direction than that of the conventional
technique for various values of power level of the
coherent interference. Although the directions of the
desired signal and the interfering signal were changed,
Fig.15 illustrates that the proposed technique can still be
more effective than the conventional technique.
In case of both Fig.14 and Fig.15, the values of the
adjustable multipliers (B and C) were changed in the
opposite manner with respect to the power of the received
signals that were estimated from the Capon’s minimumvariance method and spatial smoothing MUSIC method.
Otherwise, the weak signal problem would happen that
were experienced in the conventional technique, resulting
in inaccurate null direction for interference suppression
and deteriorate response in the desired direction.
Exp.1
Exp.2
Exp.3
Exp.4
Exp.5
(b)
Fig.17: LCMV Beam Pattern of Receiving Antenna for
Various Interference Power Levels when Desired and
Interference Directions are 10o and -8.46o, respectively,
(a) Conventional Technique, (b) Proposed Technique.
Furthermore, Fig.16 and Fig.17 are illustrated in the
case of the LCMV beamforming with the coherent
received signals. These experimental results still give the
same trend as Fig.14 and Fig.15 of the Applebaum array.
Those clarify that the proposed technique can be used
with the LCMV method as well, and the performance is
greater than the conventional technique in various
situations of coherent received signal power level.
6. CONCLUSIONS
This paper proposes the covariance matrix
adjustment technique to improve the beamforming system
of an adaptive array to solve the weak signal problem
when the SNR of the received signal is low. The feature
of this method is the efficiency of setting the beam peak
in the desired signal and null in the interference
36
ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS, VOL. 1, NO. 1, AUGUST 2003
directions. The computer simulation results and the
experimental results show that the proposed covariance
matrix adjustment technique can improve interference
cancellation in the adaptive array beamforming method
which uses covariance matrix for the complex weight
computation: Applebaum array and LCMV method.
Although, there are many incident interference signals as
much as the degree of freedom of the system, the
proposed covariance matrix adjustment technique can
solve the weak signal problem for the interference
rejection. Moreover, this technique can be effectively
used with many element numbers of receiving array
antenna and also applied with both noncoherent and
coherent received signals.
7. ACKNOWLEDGEMENT
The authors are grateful to Dr. Hiroyuki Tsuji and Dr.
Ryu Miura of Yokosuka Radio Communications
Research Center, Communications Research Laboratory
(CRL), Japan, for their invaluable comment. The Public
Management, Ministry Home Affairs, Posts and
Telecommunications (MPHPT) is acknowledged for
supporting this research.
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[3] B. R. Breed, “A Short Proof of the Equivalence of
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[7] K. Hugl, J. Laurila and E. Bonek, “Downlink
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Thanakorn Sukhonthaphong was
born in Nakhonratchasima province,
Thailand, in 1980. He received B.Eng.
from School of Telecommunication
Engineering, Suranaree University of
Technology
(SUT),
Nakhonratchasima, Thailand, in 2001.
He is currently pursuing the Master’s
degree at the Faculty of Engineering, King Mongkut's
Institute of Technology Ladkrabang (KMITL), Bangkok,
Thailand. In 2002, he did the research with the Wireless
Innovation Systems Group of the Communication
Research Laboratory (CRL), supporting by the Public
Management, Ministry Home Affairs, Posts and
Telecommunications (MPHPT), Japan. His research
interests include smart antenna, beamforming system of
adaptive array antenna and DOA estimation.
Phaisan Ngamjanyaporn was born in
Chonburi province, Thailand, in 1977.
He received B.Eng. and M.Eng. from
Faculty
of
Engineering,
King
Mongkut's Institute of Technology
Ladkrabang (KMITL) in 1998 and
2001, respectively. He is currently
pursuing the D.Eng. degree at the same institute. He is
granted by the Thailand Research Fund (TRF) through the
SUKHONTHAPHONG et al.:
COVARIANCE MATRIX ADJUSTMENT FOR INTERFERENCE CANCELLATION IMPROVEMENT … 37
Royal Golden Jubilee Ph.D. program (RGJ-Ph.D.). His
research interests include switched beam antenna, phased
array antenna and antenna for mobile communication
systems.
Chuwong Phongcharoenpanich was
born on Sept.11, 1974. He received
B.Eng., M.Eng. and D.Eng. from
Faculty
of
Engineering,
King
Mongkut’s Institute of Technology
Ladkrabang (KMITL) in 1996, 1998
and 2001, respectively. He is currently
a
lecturer
at
Department
of
Telecommunication Engineering, King Mongkut’s
Institute of Technology Ladkrabang (KMITL) and serves
as the assistant leader of Wireless Communication
Laboratory, Research center for Communications and
Information Technology at the same institute. His
research interests are antennas for mobile and wireless
communications, conformal antennas and array theory.
He is a member of ECTI, IEICE and IEEE.
Monai Krairiksh was born in
Bangkok. He received B.Eng., M.Eng.
and D. Eng. from King Mongkut’s
Institute of Technology Ladkrabang
(KMITL) in 1981, 1984 and 1994,
respectively. In 1981, he joined the
KMITL and is presently an associate
professor in the Department of
Telecommunication Engineering and serves as the leader
of Wireless Communication Laboratory, Research Center
for Communications and Information Technology at the
same institute. His main research interests are in antennas
for mobile communications, steerable beam antenna and
microwave for biological and industrial applications. He
is a member of ECTI, IEICE and IEEE.
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