An Improved Method for Decision-Directed Blind Equalization Algorithm
AN IMPROVED METHOD FOR DECISION-DIRECTED
BLIND EQUALIZATION ALGORITHM
Seongmin Kim, Wangrok Oh and Whanwoo Kim
Department of Electronics Engineering
Chungnam National University, Daejeon, Republic of Korea wwkim@cnu.ac.kr ABSTRACT s ( n)
u ( n)
ˆ( s n)
y ( n)
The decision-directed blind equalization algorithm is often used due to its simplicity and good convergence property when the eye pattern is open. However, in a channel where the eye pattern is closed, the decision-directed algorithm is not guaranteed to converge. Hence, a modified decision-directed algorithm using a hyperbolic tangent function for zero-memory nonlinear function has been proposed and applied to avoid this problem by Filho et al. But application of this algorithm includes the calculation of hyperbolic tangent function and its derivative or a look-up table which may need a large amount of memory due to channel variations. To reduce the computational and/or hardware complexity of Filho’s algorithm, in this paper, an improved method for the decision-directed algorithm is proposed. It is shown that the proposed scheme, when it is combined with decisiondirected algorithm, reduces the computational complexity drastically while it retains the convergence and steadystate performance of the Filho’s algorithm.
Figure 1. Block diagram of a receiver system using
Bussgang blind equalization algorithm.
Index Terms— Blind equalization, decision-directed, zero-memory nonlinear function.
where W ( n ) = [ w 0 ( n ) … w L −1 ( n ) ] is the tap weight
1. INTRODUCTION
e( n)
η [ ⋅ ]
Bussgang algorithm can be described by the following equations:
W ( n + 1) = W (n) + μ ( n)e( n) U ( n)
(1)
e(n) = η ⎡ y ( n )⎤ − y ( n )
⎣
⎦
(2)
T
y (n) = U (n) W(n)
(3)
T
vector of the equalizer at time n , (here, w i ( n) is the i -th equalizer coefficient), L is the equalizer length, μ (n) is the step size; U ( n ) = [u ( n ) u ( n − 1) … u ( n
BLIND EQUALIZATION ALGORITHM
Seongmin Kim, Wangrok Oh and Whanwoo Kim
Department of Electronics Engineering
Chungnam National University, Daejeon, Republic of Korea wwkim@cnu.ac.kr ABSTRACT s ( n)
u ( n)
ˆ( s n)
y ( n)
The decision-directed blind equalization algorithm is often used due to its simplicity and good convergence property when the eye pattern is open. However, in a channel where the eye pattern is closed, the decision-directed algorithm is not guaranteed to converge. Hence, a modified decision-directed algorithm using a hyperbolic tangent function for zero-memory nonlinear function has been proposed and applied to avoid this problem by Filho et al. But application of this algorithm includes the calculation of hyperbolic tangent function and its derivative or a look-up table which may need a large amount of memory due to channel variations. To reduce the computational and/or hardware complexity of Filho’s algorithm, in this paper, an improved method for the decision-directed algorithm is proposed. It is shown that the proposed scheme, when it is combined with decisiondirected algorithm, reduces the computational complexity drastically while it retains the convergence and steadystate performance of the Filho’s algorithm.
Figure 1. Block diagram of a receiver system using
Bussgang blind equalization algorithm.
Index Terms— Blind equalization, decision-directed, zero-memory nonlinear function.
where W ( n ) = [ w 0 ( n ) … w L −1 ( n ) ] is the tap weight
1. INTRODUCTION
e( n)
η [ ⋅ ]
Bussgang algorithm can be described by the following equations:
W ( n + 1) = W (n) + μ ( n)e( n) U ( n)
(1)
e(n) = η ⎡ y ( n )⎤ − y ( n )
⎣
⎦
(2)
T
y (n) = U (n) W(n)
(3)
T
vector of the equalizer at time n , (here, w i ( n) is the i -th equalizer coefficient), L is the equalizer length, μ (n) is the step size; U ( n ) = [u ( n ) u ( n − 1) … u ( n
References: vol. 3, pp. 2269 - 2272, Apr. 1997. 81, no. 10, pp. 2131-2153, 2001. Algorithms", SPIE-91., Adaptive Signal Processing, vol. 1565, pp.