TY - JOUR

T1 - Fast blind equalization using a block-updated algorithm

AU - Wu, Wen-Rong

PY - 1994/11/1

Y1 - 1994/11/1

N2 - A blind equalizer can converge without using any a priori known training sequence. The main drawback of using a blind equalizer is its slow convergence. This is due to the LMS type of algorithm employed in the equalization. The LMS algorithm uses a stochastic gradient, and the search is not necessarily in the direction of the steepest descent. This is particularly true for the blind equalizer. In this paper, we propose a new scheme to accelerate the convergent speed. The conventional blind equalization schemes are derived by minimizing the mean of some non-quadratic cost functions. Instead of doing so, we propose to minimize the time average of the cost functions. This is similar to the concept of the least square method. However, since the cost function is non-quadratic, a recursive formula cannot be obtained. Thus, we use a sub-optimal approach. We first partition the inputs into blocks. The optimal solution in a block is found by some iteration method. Its initial value is obtained from the optimal solutions of previous blocks. We also investigate the relation of our algorithm and Bussgang's. Simulations show that our algorithm significantly improves the convergent rate of blind equalizers.

AB - A blind equalizer can converge without using any a priori known training sequence. The main drawback of using a blind equalizer is its slow convergence. This is due to the LMS type of algorithm employed in the equalization. The LMS algorithm uses a stochastic gradient, and the search is not necessarily in the direction of the steepest descent. This is particularly true for the blind equalizer. In this paper, we propose a new scheme to accelerate the convergent speed. The conventional blind equalization schemes are derived by minimizing the mean of some non-quadratic cost functions. Instead of doing so, we propose to minimize the time average of the cost functions. This is similar to the concept of the least square method. However, since the cost function is non-quadratic, a recursive formula cannot be obtained. Thus, we use a sub-optimal approach. We first partition the inputs into blocks. The optimal solution in a block is found by some iteration method. Its initial value is obtained from the optimal solutions of previous blocks. We also investigate the relation of our algorithm and Bussgang's. Simulations show that our algorithm significantly improves the convergent rate of blind equalizers.

UR - http://www.scopus.com/inward/record.url?scp=0028546818&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0028546818

SN - 0255-6588

VL - 18

SP - 584

EP - 594

JO - Proceedings of the National Science Council, Republic of China, Part A: Physical Science and Engineering

JF - Proceedings of the National Science Council, Republic of China, Part A: Physical Science and Engineering

IS - 6

ER -