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 Total Downloads : 239
 Authors : Sanjay Kumar Singh
 Paper ID : IJERTV2IS111022
 Volume & Issue : Volume 02, Issue 11 (November 2013)
 Published (First Online): 26112013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
An Affine Projection Algorithm with Variable Step Size for Channel Equalization
Sanjay Kumar Singh Electronics Engineering
Board of Technical Education, Lucknow Uttar Pradesh, India.
AbstractThe recent digital transmission system demands the channel equalizers to have short training time and high tracking rate. The purpose of an adaptive equalizer is to operate on the channel output such that the cascade connection of the channel and the equalizer provides an approximation to an ideal transmission medium. The adaptive filtering algorithm employed in the equalizer design suffers from the convergence speed misadjustment trade off problem. In this paper, variants of affine projection algorithm (APA) providing fast convergence and minimum misadjustment has been presented. Simulation results are provided to corroborate the analytical results.
Index TermsAffine projection algorithm (APA),Channel equalization, Least Mean Square algorithm (LMS), Least Square algorithm (LS), normalized LMS,

INTRODUCTION
One of the most important advantages of digital transmission system is their higher reliability in noisy environment compared to their analog counter parts. Unfortunately the digital transmission suffers from inter symbol interference (ISI) where the transmitted pulses are smeared out so that the pulses that corresponds to different symbols are not separable. In order to solve this problem equalizers are designed which is meant to work in such a way that the BER (bit error rate) should be low and SNR (signal to noise ratio) should be high.
Since the channels transfer function may be stationary or nonstationary so adaptive [1, 2] equalizers are exploited mostly. An adaptive equalizer is an equalization filter that automatically adapts to time varying properties of the communication channel. It is a filter that selfadjusts its filter coefficients according to an optimizing algorithm.
The rest of the paper is organized as follows. Section II describes the basic concept of transversal equalizer. In section III the conventional affine projection algorithm is discussed. Section IV describes the variants of APA. Section V provides the experimental results and section VI presents the conclusions.

CHANNEL EQUALIZATION
The inter symbol interference (ISI) imposes obstacle in achieving increased digital transmission rates with the required accuracy. ISI problem is resolved by channel
equalization. The channel parameters are not known in advance and moreover they may vary with time. Hence it is necessary to use the adaptive equalizers, which provide the means of tracking the channel characteristics. The following figure shows a diagram of a channel equalization system.
Fig. 1. Adaptive equalizer in a chain of the transmission system
The source block transmits QPSK symbols xk Â±1Â±j1 (k=1 K) with equal probability. The total number of transmitted symbols is denoted as K. The channel block introduces ISI using a finite impulse response (FIR) type of channel model. At the output of channel, a noise sequence nk is added. This noise is assumed to be additive white Gaussian noise (AWGN) with variance n2. The sum of channel output and noise sequence forms the received signal rk, which is fed into equalizer block. Finally the equalizer output qk is fed to the slicer to obtain estimate zk of the transmitted data symbol xk. The equalizer block performs the equalization of channel. Different performance criterion can be utilized in equalization. For Mean Square Error (MSE) criterion, the LMS algorithm update of the equalizer coefficient vector is given by
hk+1 = hk + 2Âµekrk
where rk=[rk rk1 rk(N1) ]T is the input vector, hk is the weight vector, ek is the error signal, N is the number of adaptive filter coefficients and Âµ is the step size parameter. The step size parameter Âµ controls the adaption speed of the adaptive filter.
In order to implement the adaptive equalizer, we need to generate a reference signal for the adaptive algorithm. For the initial adaption of the filter coefficients we need at the receiver to be able to generate a replica of the transmitted data sequence. This known sequence is referred to as the
training sequence. During the training period the desired signal is used as a reference signal and the error signal is defined as ek = xkD – qk (see Fig. 1.). After the training period, the equalization can be performed in decision directed manner. This mode can be utilized if the channel can be assumed to be time variant. Therefore, it can be assumed that the decisions in the slicer output are correct most of the time and the slicer decisions can be used as reference signal. In the decision directed mode, the error signal is defined as ek = zk – qk (see Fig. 1.). The mean square
error (MSE) [23] for the filter in the kth time instant is
defined as
MSEk = E[ek2]

AFFINE PROJECTION ALGORITHM
The algorithm based on Least Square (LS) solution is a
e(n) is a vector of size PÃ—1 given by
e(n)=d(n)U(n)w(n) (4)
P is projection order which is number of input vectors and
is the stepsize. P and Âµ affects the performance of APA.

VARIANTS OF AFFINE PROJECTION ALGORITHM
The low cost APA [19] includes fast affine projection algorithm (FAP), Gauss Seidel pseudo affine projection algorithm (PAP), and low complexity dichotomous coordinate descent (DCD) based APA, FAP[24] and PAP. The variable step size (VSS) based APA are discussed below.

Optimal variable step size Affine Projection Algorithm
The update recursion (2) can be written in terms of the
member of zero forcing algorithms (ZFA). ZFA may provide
weighterror vector,
w(n) wo w(n)
as:
the convergence of the adaptive filter in M iterations (M is
w(n 1) w(n) U* (n)[U(n)U* (n)]1e(n)
(5)
the filter length) by solving the MÃ—M full rank equation matrix. There is another member of ZFA, which is base on partial rank filtering equations. This algorithm is known as affine projection algorithm (APA)[15]. It is also called as
partial rank algorithm. Ehen the APA provides full rank
Squaring both sides and taking expectations, we find that that the mean square deviation (MSD)[2] satisfies:
E w(n 1) 2 E w(n) 2 2 ReE e* (n)(U(n)U*(n))1 U(n)w(n)
2 E e* (n) U(n)U* (n)1 e(n)
solution, it becomes equivalent to LS algorithm and may converge in M iterations. The APA provides slower
E w(n) 2 ()
(6)
convergence [16] with lower projection orders (partial rank solutions), and the convergence speed is also highly dependent on the correlation of the input process. The Affine Projection Algorithm with projection order one is equivalent
to normalized LMS algorithm (NLMS) [19].
If we choose such that () is maximized, then this
choice guarantees that the MSD will undergo the largest decrease from iteration n to n+1 .
Maximizing
() 2 ReE e* (n)(U(n)U*(n))1 U(n)w(n) 2 E e*(n) U(n)U*(n)1 e(n)
with respect to , leads to the optimum stepsize
Re E e* (n) U(n)U* (n) 1 U(n)w(n)
(7)
o (n)
E e* (n) U(n)U* (n) 1 e(n)
Assuming the noise sequence (n) is identically and independently distributed and statistically independent of the regression data {U(n)}, neglecting the dependency of w(n) on past noises, o (n) is approximated as
Fig. 2. Adaptive Scheme and signals involved using APA
o(n)
E w(n) 2
(8)
E w(n) 2 2Tr E U(n)U* (n) 1
Consider data {d(n)} that arise from the model
d (n) u(n)wo (n)
(1)
v
v
where
o E w(n) 2 E w* (n)U* (n) U(n)U* (n)1 U(n)w(n)
where w is an unknown column vector that we wish to
estimate, (n) accounts for measurement noise and u(n)
Observe that
U* (n) U(n)U* (n)1 U(n)
is a projection
denotes 1XM row input (regressor) vectors. Let w(n) be an
estimate for wo at iteration n. The Affine Projection
matrix onto R(U*(n)) ,the range space of U*(n).
Algorithm computes w(n) via:
Let
p(n)
U* (n) U(n)U* (n)1 U(n)w(n)
(9)
w(n 1) w(n) U* (n)[U(n)U* (n)]1e(n)
where
(2)
which is the projection of w(n)
Since
onto R(U*(n)).
U(n) [u(n)u(n 1)……u(n P 1)]T
d(n) [d (n)d (n 1)…….d (n P 1)]T
(3)
p(n) 2 w* (n)U* (n) U(n)U* (n)1 U(n)w(n) (10) therefore the optimum stepsize in (8) becomes
o (n)
E p(n) 2
(11)
The newly generated projection matrix in (17) is time dependent; the latest data are more significant in the pseudo
v
v
E p(n) 2 2Tr E U(n)U* (n)
In calculating this o (n) , however, the major obstacle is
inverse matrix by which the error vector is projected.
The variable step size Affine Projection Algorithm with forgetting factor (VSAPAFF) is:
that p(n) is not available during adaptation, since wo is
unknown.
2
2
w(n 1) w(n) (n)U* (n)[U(n)U* (n)]1 e(n)

Variable step size Affine Projection Algorithm
(n)
p ' (n)
When v (i) =0, p(n)=U*(n)(U(n)U*(n))1e(n) and even with white noise, it holds under expectation that
max
p ' (n) C
2
2
1
(20)
* * 1
(12)
p ' (n) p ' (n 1) (1 )U'*(n) U' (n)U'*(n)
e(n)
E[p(n)] E U (n) U(n)U (n) e(n)
Motivated by these facts, we estimate p(n) by time averaging as follows:
p (n) p (n 1) (1 )U* (n) U(n)U* (n)1 e(n) (13)
0 < 1 Note that U(n) is only replaced by U' (n) during the error evaluation phase (19), not during the weights updating phase because of instability which has been observed in some
with a smoothing factor (0<1).
simulations of replacing U(n) by
U' (n)
for both. This
Using
p (n) 2
instead of
E p (n) 2
in (11), the VSS
phenomenon is most possibly due to the illconditioning of
APA becomes
w(n 1) w(n) (n)U* (n)[U(n)U* (n)]1e(n)
p (n) 2
(n) max 2
(14)
the input matrix U(n) caused by forgetting process.
A special case of this Algorithm is the variable step size NLMS with forgetting factor (VSNLMSFF) obtained by setting K = 1. For this case, the input matrix U(n) is a row
vector and the forgetting factor processing is implemented
p (n) C
only in the row direction.
where C is a positive constant.
v
v
From (11) and (14), we see that C is related to 2TrE U(n)U* (n), and this quantity can be
approximated as K/ SNR.
When p (n) 2 is large, w (n) is far from w0 and (n) tends
U' (n) = U(n)(L) (21)
D.Regularization of ill conditioned Projection Matrix
In (19) of the previously proposed Algorithm,
to . On the other hand when
p (n) 2
is small, w(n)
( U' (n)U'* (n)
) is potentially illconditioned with small
max
approaches w0 and stepsize is small. Thus depending on
singular values. Using the singular value decomposition
U'
p (n) 2 , (n) varies between 0 and max .
To guarantee Filter stability [14], max is chosen less than 2
C. Optimal variable step size APA with Forgetting Factor
Now, we introduce a forgetting factor into the pseudo inverse projection matrix, resulting in a marked convergence enhancement. The input matrix at time n can be described as:
(SVD), can be decomposed as:
U' =RV (22)
where R and V are KÃ—K and LÃ—L unitary matrices, respectively. is a KÃ—L matrix with nonnegative diagonal elements of singular values n, The illconditionness of U is characterized by its condition number,
condU=max/min=1/K (23) from (17), the SVD of the weighted input matrix U' is:
U' = (K)U(L) = (K)[RV](L)
U[k 1,l 1] (n) u(n k l)
k = 0, 1, . . ., K 1; l = 0, 1, . . . , L 1;
By introducing a forgetting factor , 0 < 1,
(15)
= R((K)(L))V (24)
= R ' V
where ' is a K Ã— L matrix with all zero entities except
[ ' ]j,j
= 2(j1)j
, j = 1, 2, . . .,K. The condition number
U' (n) u(n k l)k l ku(n k l)l
of the weighted input matrix U' is:
[k 1,l 1]In matrix notation, we represent this as
(16)
cond U'
= 1/[
2(K1)
K] =
2(1K)
condU
U
U
'
[k 1,l 1](n) K U(n)L
(17)
which illustrates the increasing condition number due to decrease in and increase in K. Because of this ill
where (m) is an m Ã— m diagonal matrix with
[(m)]j,j = j1 j = 1, 2, . . .,m (18)then (12) becomes
p' (n) U'* (n) U* (n)U'* (n)1 e(n)
conditioning, the estimated p' may not be a true evaluation of the error signal. Even if the error signal is stable, the projected p' could be unstable. Thus the VSAPA and VS
(19) APAFF Algorithms adopt a smoothing function, in the form
of (13), to alleviate this problem with the cost loss of error signal fidelity, which sacrifices convergence speed and/or misadjustment.
To address this problem we use Tikhonov regularization approach, under which (19) becomes:
p' (n) U'* (n) U* (n)U'* (n) 2 I 1 e(n)
(25)
where I is the identity matrix, and is a hyper parameter to control the amount of regularization. The modified Algorithm becomes:
w(n 1) w(n) (n)U* (n)[U(n)U* (n)]1e(n)
2
p ' (n)
(n) max 2
p ' (n) C
(26)
Note that the smoothing function is no longer needed since the regularization process accomplishes this function.


SIMULATION RESULTS
We illustrate the performance of the Affine Projection Algorithm by carrying out computer simulations for variable step sizes and projection orders. QPSK generator provides the test signal and additive white Gaussian noise (AWGN) is used as noise signal. The Filter length is chosen to be of 32 taps and the covariance matrix of offset 1 is selected.
Fig. 3 Scatter Plot for Âµ=0.01 and po=2
Fig. 4 Scatter Plot for Âµ=0.05 and po=2
Fig. 5 Scatter Plot for Âµ=0.05 and po=10

CONCLUSION
We see that as the stepsize increases the convergence speed increases but at the same time steady state error also increases. Also when projection order increases the convergence speed increases but at the same time steady state error also increases. The symbols are largely scattered when the signal is transmitted through the channel; this is due to the presence of noise, but when they pass through the equalizer the scattering of the symbols reduces. The Variable step size methods for Affine Projection Algorithm (VSS APA) improve the SNR and reduce the computational requirement.
ACKNOWLEDGMENT
The author would like to express his sincere gratitude to Associate Professor Shri Y.K.Mishra and Assistant Professor Dr. R.K.Singh at Kamla Nehru Institute of Technology, Sultanpur, India for valuable discussions.
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