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A kernel canonical correlation analysis algorithm for blind equalization of oversampled Wiener systems

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A KERNEL CANONICAL CORRELATION ANALYSIS ALGORITHMFOR BLIND EQUALIZATION OF OVERSAMPLED WIENER SYSTEMS
Steven Van Vaerenbergh, Javier V ´ ıa and Ignacio Santamar ´ ıa
{
steven,jvia,nacho
}
@gtas.dicom.unican.esDepartment of Communications EngineeringUniversity of Cantabria, Spain
ABSTRACT
In this paper we present an algorithm for blind equalizationof single-input multiple-output (SIMO) nonlinear systems, inwhich each nonlinear channel is a Wiener system. The pro-posed method combines ideas from blind linear SIMO iden-tiﬁcation with kernel canonical correlation analysis (kernelCCA) to identify the nonlinearities. It is shown in the paperthat the blind equalization problem can be solved in an itera-tive manner, alternating between a CCA problem (to estimatethe linear ﬁlters) and a kernel CCA problem (to estimate thememoryless nonlinearities). The resulting algorithm can beapplied to the general case of nonlinear SIMO systems with
P
outputs. Simulations are included to demonstrate its effec-tiveness.
1. INTRODUCTION
In the last decade there has been a great interest in blind iden-tiﬁcation and equalization methods. In digital communica-tions, blind methods permit channel identiﬁcation or equal-ization without the need to send known training signals, thussaving bandwidth. In particular, the problem of blind identiﬁ-cationofsingle-inputmultiple-output(SIMO)linear channelshas received considerable attention [1, 2]. In this case, blindidentiﬁcation can be accomplished by resorting only to thesecond-order statistics (SOS) of the channel output.While a lot of attention has gone to the analysis of linearSIMOsystems, manyreal-lifesystems exhibitnonlinearchar-acteristics. Recently, a growing amount of research has beenconducted on nonlinear system identiﬁcation [3]. Nonlineardynamical system models generally have a high number of parameters, although many problems can be sufﬁciently wellapproximated by simpliﬁed block-based models. The modelconsisting of a cascade of a linear dynamic system and amemoryless nonlinearity is known as the Wiener system, asillustrated in Fig. 1. Wiener systems are frequently used in
This work was supported by MEC (Ministerio de Educaci´on y Ciencia)under grant TEC2007-68020-C04-02 TCM (MultiMIMO) and FPU grantAP2005-5366.
s
[
n
]
n
[
n
]
x
[
n
]
y
[
n
]
H
(
z
)
f
(
·
)
Fig. 1
. A Wiener system with additive noise
n
[
n
]
.contexts such as digital satellite communications [4], opticalﬁbre communications [5] and digital magnetic recording.A number of supervised approaches have been proposedto identify or equalize these systems, ranging from black-boxapproachesusingdifferenttypesofstructuresandtrainingcri-teria [6, 7], to approaches that explicitly exploit the systemstructure [8, 9, 10]. However, very little work has been doneon blind identiﬁcation methods. Blind methods generally as-sume some knowledgeon the input signal statistics and/or thechannel model. A few blind methods to identify Wiener sys-tems can be found in [11, 12]. Blind identiﬁcation methodsfor other nonlinearsystems such as Volterra models have alsobeen presented in [13, 14].In this paper we present a blind method to identify andequalize nonlinear SIMO systems that consist of variousWiener systems, as illustrated in Fig. 2. These systems couldrepresent a sensor array in which every sensor exhibits a non-linear behavior, or they could be obtained by oversamplingthe output of a nonlinear communications channel [1]. Thepresentedmethodcombines ideas from the blind linear SIMOidentiﬁcation method in [1] and from the supervised nonlin-ear equalization technique discussed in [9]. It performs atthe same time a kernel-based regression to learn the nonlin-earities and a least-squares (LS) method to retrieve the linearchannels. The assumptions made by our method are thatevery nonlinearity in the SIMO Wiener system is invertible,that the linear channels share no common zeros and, ﬁnally,that the maximum channel order is known.
s
[
n
]
n
1
[
n
]
n
2
[
n
]
x
1
[
n
]
x
2
[
n
]
y
1
[
n
]
y
2
[
n
]
H
1
(
z
)
H
2
(
z
)
f
1
(
·
)
f
2
(
·
)
Fig. 2
. A SIMO system consisting of 2 Wiener systems.
2. PROBLEM SETTING
Consider a nonlinear SIMO system, in which each channelis modeled as a Wiener system. An example with only twooutputs is shown in Fig. 2. In a general case with
P
outputsthe system can be modeled as
y
i
[
n
] =
L
−
1
j
=0
h
i
[
j
]
s
[
n
−
j
]
(1)
x
i
[
n
] =
f
i
(
y
i
[
n
]) +
n
i
[
n
]
,
(2)where
s
[
n
]
represents the input symbol sent at time instant
n
,
h
i
[
j
]
is the
j
-th coefﬁcient of the
i
-th linear FIR channel
H
i
(
z
)
,
f
i
(
·
)
is the nonlinearity of channel
i
and
n
i
[
n
]
rep-resents additive Gaussian noise, for
i
= 1
,...,P
and
n
=0
,...,N
−
1
. Without loss of generality,
L
represents themaximum channel order (which we assume to be known).The problem considered in this paper is to recover thetransmitted signal
s
[
n
]
when only the output signals
x
i
[
n
]
areobserved.
3. BLIND SIMO WIENER SYSTEM EQUALIZATION
The solution we propose to the equalization problem ismainly based on the linear identiﬁcation method presented in[1]. In the following this linear method is explained brieﬂyfor a
1
×
2
linear SIMO system, although the generalizationto more output channels is straightforward.
3.1. Blind identiﬁcation of a linear SIMO system
Taking a block of
N
observations, deﬁne the matrix
X
i
=
x
i
[
n
+
L
−
1]
···
x
i
[
n
]
.........
x
i
[
n
+
N
−
1]
···
x
i
[
n
+
N
−
L
]
,
(3)for
i
= 1
,
2
. Denoting the estimate of the channel impulseresponse vectors as
ˆ
h
i
=
ˆ
h
i
[0]
,...,
ˆ
h
i
[
L
−
1]
T
,
(4)
s
[
n
]
x
1
[
n
]
x
2
[
n
]
H
1
(
z
)
H
2
(
z
)ˆ
H
1
(
z
)ˆ
H
2
(
z
)
e
[
n
]
Fig.3
. A blindidentiﬁcationschemefora linearSIMO modelwithout noise.it can be easily veriﬁed that in a noiseless case the solutionshould satisfy
X
1
ˆ
h
2
=
X
2
ˆ
h
1
,
(5)as illustrated in Fig. 3.In order to avoid the trivial solution
ˆ
h
i
=
0
, a restrictionmust be applied to the solution. Typical restrictions in com-munications are either to ﬁx the norm of the ﬁlters
ˆ
h
i
, or toﬁx the norm of the output signal
X
i
ˆ
h
j
.A restriction on the ﬁlter norm was used in [1] to developthe LS method, also referred to as cross-relation. This prob-lem consists of minimizing the cost function
J
LS
=12
X
1
ˆ
h
2
−
X
2
ˆ
h
1
2
s.t.
ˆ
h
1
2
+
ˆ
h
2
2
= 1
,
(6)which is equivalent to the following eigenvalue problem
X
T
1
X
1
−
X
T
1
X
2
−
X
T
2
X
1
X
T
2
X
2
ˆ
h
=
β
ˆ
h
.
(7)The solution
ˆ
h
= [ˆ
h
T
2
,
ˆ
h
T
1
]
T
is found as the eigenvector cor-responding to the smallest eigenvalue.If, instead, the constraint is applied to the norm of outputsignals as in [2], the cost function to minimize turns out to be
J
CCA
=12
X
1
ˆ
h
2
−
X
2
ˆ
h
1
2
s.t.
X
1
ˆ
h
2
2
+
X
2
ˆ
h
1
2
= 1
.
(8)This is a canonical correlation analysis (CCA) problem, andits solutionis givenbythe principaleigenvectorofthe follow-ing generalized eigenvalue problem (GEV)
X
T
1
X
1
X
T
1
X
2
X
T
2
X
1
X
T
2
X
2
ˆ
h
=
β
X
T
1
X
1
00 X
T
2
X
2
ˆ
h
.
(9)Once the channels
ˆ
h
1
and
ˆ
h
2
have been estimated, they canbe used to obtain an equalizer by applying the zero-forcing(ZF) or the minimum mean square error (MMSE) approach.NotethatboththeLSalgorithmandtheCCA-basedalgorithmrequireknowledgeof themaximumchannelorder
L
, andtheyassume the linear channels share no common zeroes.When we consider that each channel of the system is re-placed by a Wiener system, the scheme of Fig. 4 can be usedfor blind identiﬁcation, where the inﬂuence of the nonlinear-ities
f
i
(
·
)
is removed ﬁrst, by estimating the inverse nonlin-earities
g
i
(
·
) =ˆ
f
−
1
i
(
·
)
.
s
[
n
]
v
1
[
n
]
v
2
[
n
]
x
1
[
n
]
x
2
[
n
]
y
1
[
n
]
y
2
[
n
]
H
1
(
z
)
H
2
(
z
)
f
1
(
·
)
f
2
(
·
)
g
1
(
·
)
g
2
(
·
)
ˆ
y
1
[
n
]ˆ
y
2
[
n
]ˆ
H
1
(
z
)ˆ
H
2
(
z
)
z
12
[
n
]
z
21
[
n
]
e
12
[
n
]
Fig. 4
. The identiﬁcation diagram for a SIMO system consisting of 2 Wiener subsystems, in which
g
i
(
·
) =ˆ
f
−
1
i
(
·
)
.To estimate the inverse nonlinearities
g
i
(
·
)
, we will ap-ply a nonparametric identiﬁcation approach based on kernelmethods. Nonparametric approaches do not assume that thenonlinearity corresponds to a given model, but rather let thetraining data decide which characteristic ﬁts them best.
3.2. Nonlinear regression with kernel methods
Kernel methods [15] are based on a nonlinear transformation
Φ
of the data from the input space to a high-dimensional
fea-ture space
H
, where it is more likely that a problem can besolved in a linear manner,
Φ
:
R
m
→ H
Φ(
x
) =˜
x
.
Scalar products in feature space can be calculated without theexplicit knowledge of the nonlinear transformation
Φ
, by ap-plying the corresponding
kernel function
κ
(
·
,
·
)
on pairs of data points in the input space,
κ
(
x
i
,
x
j
) :=
˜
x
i
,
˜
x
j
=
Φ(
x
i
)
,
Φ(
x
j
)
.
(10)This property, which is known as the “kernel trick”, allowsto perform any scalar product-based algorithm in the featurespace by solely replacing the scalar products with the kernelfunction in the input space.Most kernel algorithms use a
kernel matrix
K
i
, which isconstructedbyapplyingthekernelfunctiononpairsofpoints:
k
i
(
m,n
) =
κ
(
x
i
[
m
]
,x
i
[
n
])
, with
m,n
= 1
,...,N
. An oftenused kernel function is the Gaussian kernel with width
σκ
(
x
i
,
x
j
) = exp
−
x
i
−
x
j
2
2
σ
2
,
which implies an inﬁnite dimensional feature space [15].Nonlinear regression with kernels is possible by repre-senting the nonlinearity as a
kernel expansion
ˆ
y
i
[
n
] =
g
i
(
x
i
[
n
]) =
M
m
=1
ˆ
α
i
[
m
]
κ
(
x
i
[
n
]
,x
si
[
m
])
,
(11)where
x
si
[
m
]
are called the
support vectors
of the nonlin-ear representation. In the following we will use the variable
k
si
(
n,m
) =
κ
(
x
i
[
n
]
,x
si
[
m
])
to simplify the notation. In aﬁrst approach, all available points
x
i
[
n
]
will be used as sup-port vectors, i.e.,
M
=
N
.At this point it should be clear that once the inverse non-linearities
g
i
(
·
)
have been estimated, retrieval of the linearFIR channels
ˆ
h
i
is straightforward through a linear SIMOidentiﬁcation technique such as the CCA- or LS-based algo-rithms. Given only the outputs
x
i
[
n
]
of the system, directestimation of these nonlinearities is difﬁcult, however, sinceno information on the input signal
s
[
n
]
is available.Therefore,since separateestimation ofthe linearandnon-linear parts of this system is difﬁcult, we will design an algo-rithm that allows us to obtain both the linear ﬁlters and thenonlinearities simultaneously, through a single cost function.
3.3. Proposed cost function
First, we will treat the case where the observed system hasonly two outputs. Given the representation of the nonlinear-ity
g
i
(
·
)
as in (11), the output of the proposed identiﬁcationscheme can be written as
z
12
[
n
] =
L
−
1
i
=0
M
m
=1
ˆ
h
2
[
i
]
k
s
1
(
n
−
i,m
)ˆ
α
1
[
m
]
.
(12)In matrix notation, this becomes
z
12
[
n
] =ˆ
h
T
2
K
1
[
n
]
ˆ
α
1
,
(13)where the
l
-th row of
K
1
[
n
]
contains the elements from
k
s
1
(
n
+
l
−
1
,
1)
till
k
s
1
(
n
+
l
−
1
,M
)
. The expression for
z
21
[
n
]
is found in the same manner. Combining
N
outputsamples of each channel into the vectors
z
12
and
z
21
, weobtain the following cost function to minimize:
J
2
=
z
12
−
z
21
2
s.t.
z
12
2
+
z
21
2
= 1
.
(14)
3.4. Proposed iterative solution
The minimization problem (14) has no direct analytical so-lution. However, if
ˆ
α
1
and
ˆ
α
2
were available, it would bepossible to obtain the correspondingoptimal ﬁlters
ˆ
h
2
and
ˆ
h
1
by applying linear CCA. Moreover, since we are represent-ing the nonlinearities
g
1
(
·
)
and
g
2
(
·
)
as linear combinationsof support vectors, a similar operation can be carried out toestimate these: if
ˆ
h
2
and
ˆ
h
1
are given, (14) can be solved toﬁnd the optimal coefﬁcients of the kernel expansions
ˆ
α
1
and
ˆ
α
2
. This suggests an iterative scheme that alternates betweenupdating the linear channels
ˆ
h
i
and the memoryless nonlin-earities
ˆ
α
i
. Convergence is guaranteed because each updatemay either decrease or maintain the cost.
3.4.1. Iteration 1: given
ˆ
α
i
, obtain
ˆ
h
i
If estimates of
ˆ
α
1
and
ˆ
α
2
are given, Eq. (12) shows that theoutput
z
12
[
n
]
of the identiﬁcation scheme can be obtained as
z
12
[
n
] =
L
−
1
i
=0
ˆ
h
2
[
i
]ˆ
y
1
[
n
−
i
]
,
(15)where
ˆ
y
1
[
n
−
i
]
is calculated with (11). In matrix form thiscan be written as
z
12
=ˆ
Y
1
ˆ
h
2
, where
n
-th row of the matrix
ˆ
Y
1
contains the elements from
ˆ
y
1
[
n
]
until
ˆ
y
1
[
n
+
L
−
1]
. Theminimization problem (14) can be rewritten as minimizing
J
h
=
ˆ
Y
1
ˆ
h
2
−
ˆ
Y
2
ˆ
h
1
2
s.t.
ˆ
Y
1
ˆ
h
2
2
+
ˆ
Y
2
ˆ
h
1
2
= 1
,
(16)which can be solved by standard linear CCA.
3.4.2. Iteration 2: given
ˆ
h
i
, obtain
ˆ
α
i
If estimates of
ˆ
h
1
and
ˆ
h
2
are given, Eq. (12) shows that theoutput
z
12
[
n
]
of the identiﬁcation scheme can be obtained as
z
12
[
n
] =
M
m
=1
w
1
[
n,m
]ˆ
α
1
[
m
]
,
(17)wherethe variable
w
1
[
n,m
] =
L
−
1
i
=0
ˆ
h
2
[
i
]
k
1
(
n
−
i,m
)
is in-troduced. In matrix form this can be written as
z
12
=
W
1
ˆ
α
1
,where the
n
-th row of the matrix
W
1
contains the elements
w
1
[
n,
1]
until
w
1
[
n,M
]
. The minimization problem (14) canbe rewritten as minimizing
J
ˆ
α
=
W
1
ˆ
α
1
−
W
2
ˆ
α
2
2
s.t.
W
1
ˆ
α
1
2
+
W
2
ˆ
α
2
2
= 1
.
(18)Ifall datapoints
x
i
[
n
]
areusedas supportvectorsinthekernelexpansion(11),i.e., if
M
=
N
(which implies
ˆ
α
i
∈
R
N
), thedimensionality of this problem is signiﬁcantly higher than itslinear counterpart (16). This leads to various difﬁculties.First of all, problem(18)will suffer fromoverﬁttingwhensufﬁciently “rich” kernel functions are used, i.e., kernels thatcorrespond to feature spaces whose dimension
m
′
is muchhigher than the number of available data points
N
. This oc-curs for instance for the Gaussian kernel, whose feature spaceis inﬁnite dimensional. Second, the GEV corresponding tothisproblemrequirestheretrievalofeigenvectorsof
2
N
×
2
N
Algorithm 1
Equalization algorithm for nonlinear SIMOchannels.Initialization: obtain
ˆ
h
i
by solving the LS problem (7).Construct the kernel matrices
K
i
from
x
i
[
n
]
.Perform kernel PCA to obtain the reduced matrices
W
i
.
repeat
CCA1: With given
ˆ
h
i
, update
ˆ
α
i
by solving (18).CCA2: With given
ˆ
α
i
, update
ˆ
h
i
by solving (16).
until
ConvergenceObtain
s
[
n
]
from
ˆ
y
i
[
n
]
and
ˆ
h
i
by applying linear ZF orMMSE equalizers.matrices, which in this case implies a high computationalcost.Overﬁtting is a common issue in kernel CCA that can besolved in different manners [16]. Common workarounds in-clude adding regularization to the problem or reducing thedimensionality of the problem by applying kernel PCA [17].In this case a dimensionality reduction is desired since at thesame time it will avoid overﬁtting and reduce the computa-tional load. Speciﬁcally, kernel PCA reduces the kernel ma-trix
K
i
∈
R
N
×
N
to
V
i
Σ
i
V
T i
≈
K
i
,
(19)where
Σ
i
∈
R
M
×
M
is a diagonal matrix containing the
M
largest eigenvalues of
K
i
and
V
i
∈
R
N
×
M
contains the
M
corresponding eigenvectors. This allows us to redeﬁne thevariable
w
1
[
n,m
]
as
w
1
[
n,m
] =
L
−
1
i
=0
h
2
[
i
]
v
1
(
n
−
i,m
)
,where
v
1
(
n,m
)
is the
n
-th element of the
m
-th eigenvectorin
V
1
. Thanks to this reduction, the dimensions of the matrices
W
i
in (18) are reduced to
N
×
M
, with
M
≪
N
, and thesolutions
ˆ
α
i
can be found by applying CCA.
3.5. Extensions and Further Comments
Analogously to many other iterative techniques, the perfor-mance of the proposed approach can depend on the initial-ization of the linear channels and nonlinearities. Here, wepropose to obtain an initial estimate of the linear channels
ˆ
h
i
by ﬁrst applying the LS algorithm from [1] to the outputs
x
i
[
n
]
, i.e, in the ﬁrst iteration, the estimated nonlinearities are
g
i
(
x
i
[
n
]) =
x
i
[
n
]
. Furthermore, we must note that the ﬁnaltarget of the proposed algorithm consists in recovering thesource signal
s
[
n
]
. Thus, after obtaining the outputs
ˆ
y
i
[
n
]
andthe linear channels
ˆ
h
i
, the input can be easily recovered bymeans of a linear ZF or MMSE equalizer.In the general case of a system with
P
sensors, the costfunction needs to take into account the difference betweeneachpair ofoutputs. Note that theoutputsignal
z
ij
representsthe signal
x
i
after being transformed by
g
i
(
·
)
and ﬁltered by
−2 −1 0 1 2−4−2024x
y
output x vs real youtput x vs estimated y
(a) Nonlinearity estimate.
1 2 3 4 5
−0.6−0.4−0.200.20.40.6
real channel coefficientsestimated channel coefficients
(b) Linear ﬁlter coefﬁcients.
Fig. 5
. Identiﬁcation results on the
1
×
3
Wiener SIMO sys-tem. (a) shows the noisy output
x
3
[
n
]
vs. the real internalsignal
y
3
[
n
]
, and
x
3
[
n
]
vs. the estimated
ˆ
y
3
[
n
]
. (b) shows theestimated ﬁlter coefﬁcients of
h
3
vs. the real coefﬁcients.
h
j
. The cost function to minimize now becomes
J
P
=
M
i,j
=1
i
=
j
z
ij
−
z
ji
2
s.t.
M
i,j
=1
i
=
j
z
ij
2
= 1
,
(20)and the resulting algorithm is analogous to that of the two-channel case. The ﬁnal iterative technique for
P
output chan-nels is summarized in Algorithm 1.Finally, we must note that when the SIMO system is ob-tainedbyoversampling,the
P
nonlinearitieswill be the same.Obviously, this can be exploited to obtain a more accurate es-timate. The corresponding GEV can be found easily, but it isomitted here due to space restrictions.
4. EXPERIMENTS
We experimentally tested the proposed algorithm with somenumerical examples. All tests were conducted on data sets of
N
= 256
data symbols. The fraction of the signal energy dis-cardedbythe kernelPCA procedureintheinitializationphasewas ﬁxed as
10
−
14
. The resulting number of kept eigenvec-tors was between
M
= 11
and
M
= 15
. In all experimentsconvergencewas obtained in less than
20
iterations.The ﬁrst system used is a
1
×
3
Wiener SIMO system withlinearﬁlters
h
1
= [0
.
6172
,
0
.
6247
,
0
.
3373
,
−
0
.
0349
,
−
3
.
2957]
T
,
h
2
= [
−
0
.
8601
,
0
.
1532
,
−
0
.
1888
,
−
0
.
6264
,
0
.
9985]
T
and
h
3
= [1
.
3271
,
−
0
.
1472
,
−
0
.
4786
,
0
.
6682
,
0
.
0045]
T
, respec-tively. The nonlinearity was the same for all the channels,namely
f
i
(
x
) = tanh(0
.
8
x
) + 0
.
1
x
.A ﬁrst test was conductedon this system with a zeromeanand unit variance Gaussian source. The power of the whiteGaussian noise after the nonlinearities was ﬁxed to obtain a
20
dB SNR. Fig. 5 shows the true and estimated linear ﬁlterand nonlinearity for one of the branches of the Wiener SIMOsystem, after
15
iterations of the algorithm.We then compared the proposed algorithm to the linearCCA-based equalizer from [2]. Averages were taken over
0 10 20 30 40 50 6010
−6
10
−5
10
−4
10
−3
10
−2
10
−1
10
0
SNR (dB)
M S E
linear CCA on SIMO linear systemlinear CCA on SIMO Wiener systemblind KCCA on SIMO Wiener systemsupervised KCCA on SIMO Wiener system
Fig. 6
. MSE comparison for different algorithms.
50
independent Monte-Carlo simulations, and the MSE wascalculated between the true and the estimated input signal.The results are shown in Fig. 6. The curve with solid black dots was obtained by applying the linear CCA-based equal-izer on the system that only contained the linear channels
h
1
,
h
2
and
h
3
. The same algorithm was tested on the nonlinear
1
×
3
SIMO Wiener system, resulting in the curve with whitesquares. The curve marked with circles was obtained by ap-plying the proposed blind method on the
1
×
3
SIMO Wienersystem, and in the last curve (dashed line) we show the re-sults when the supervised method of [9] was applied to thissystem. The proposed method obtains results that are veryclose to those obtained by the supervised method.For the second test we comparedthree SIMO Wiener sys-tems with different numbers of outputs. System
1
was a
1
×
2
SIMO Wiener system with
h
1
and
h
2
as deﬁned in the ﬁrstexperiment. System
2
was the discussed
1
×
3
system. Sys-tem
3
was a
1
×
4
SIMO Wiener system that included allthreepreviousWiener systems and a new linearchannel
h
4
=[
−
0
.
1155
,
−
0
.
9170
,
0
.
5605
,
0
.
4862
,
−
0
.
8004]
T
in its fourthbranch. The nonlinearity was maintained, and we exploitedthe fact that it was the same for each channel. The results areshown in Fig. 7. The same test was repeated for a systemwith a binary input
s
[
n
]
∈ {−
1
,
1
}
, but now we did not ex-ploit the information that the nonlinearity was the same foreach channel. The results are shown in Fig. 8.
5. CONCLUSIONS
We proposed a blind equalization algorithm for nonlinearSIMO systems in which every channel is a Wiener system.Basically, the method iterates between a CCA algorithm forestimating the linear channel and a KCCA algorithm for es-timating the memoryless nonlinearities. First results showthat this iterative algorithm converges fast and achieves per-formance that is very close to a related supervised method.Future research lines include a comparison to other blindnonlinear equalization methods such as [13, 14].

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