equalization Alogorithm and its analysis
Improved Robustness Adaptive Step Size LMS
Equalization Algorithm and Its Analysis
Lan-Jian Cao
Zhi-Zhong Fu
Qing-Kun Yang
Department of Communications
Department of Communications
Department of Communications
University of Electronic Science and Technology of China
University of Electronic Science and Technology of China
University of Electronic Science and Technology of China
Chengdu, China caolanjian@126.com Chengdu, China fuzz@uestc.edu.cn Chengdu, China
Yqk86@yahoo.com.cn
Abstract- In order to improve the performance of LMS (Least
Mean Square) adaptive filtering algorithm, an improved robustness adaptive step-size LMS equalization algorithm was presented by establishing a nonlinear relationship between the two relevant statistics for step-size factor μ ( n ) and the error signal e( n ) . Compared with other algorithms, this algorithm overcomes of sensitivity to the noise coming from outside by introducing the statistics for the correlation of error signal e( n ) .
Meanwhile, this algorithm presents some improvement on the principle of robustness. Theoretical analysis and simulation results indicate that this algorithm has a faster convergence speed and a better steady-state error, and can go back to steady state quickly when the channel is varying with time, which shows a better robustness and convergence than other traditional ones.
Key words- adaptive equalize; variable step-size; Robustness; least mean square; error signal
I.
signals by using the mutual-correlation between them in [2], but it has high complexity. Gelfand and Wei introduced a
SVSLMS algorithm to adjust step-size using sigmoid function in [3]. Although it has a fast convergence speed, it can not get a low steady error because its step-size can not change gently when its error signals e( n ) is close to 0. Pazaitis and Luo established a function, which is similar to sigmoid function’s fixed parameters form, by using the
Equalization Algorithm and Its Analysis
Lan-Jian Cao
Zhi-Zhong Fu
Qing-Kun Yang
Department of Communications
Department of Communications
Department of Communications
University of Electronic Science and Technology of China
University of Electronic Science and Technology of China
University of Electronic Science and Technology of China
Chengdu, China caolanjian@126.com Chengdu, China fuzz@uestc.edu.cn Chengdu, China
Yqk86@yahoo.com.cn
Abstract- In order to improve the performance of LMS (Least
Mean Square) adaptive filtering algorithm, an improved robustness adaptive step-size LMS equalization algorithm was presented by establishing a nonlinear relationship between the two relevant statistics for step-size factor μ ( n ) and the error signal e( n ) . Compared with other algorithms, this algorithm overcomes of sensitivity to the noise coming from outside by introducing the statistics for the correlation of error signal e( n ) .
Meanwhile, this algorithm presents some improvement on the principle of robustness. Theoretical analysis and simulation results indicate that this algorithm has a faster convergence speed and a better steady-state error, and can go back to steady state quickly when the channel is varying with time, which shows a better robustness and convergence than other traditional ones.
Key words- adaptive equalize; variable step-size; Robustness; least mean square; error signal
I.
signals by using the mutual-correlation between them in [2], but it has high complexity. Gelfand and Wei introduced a
SVSLMS algorithm to adjust step-size using sigmoid function in [3]. Although it has a fast convergence speed, it can not get a low steady error because its step-size can not change gently when its error signals e( n ) is close to 0. Pazaitis and Luo established a function, which is similar to sigmoid function’s fixed parameters form, by using the
References: processing (S0096-3518), 1991, 35(1): 122-127. Simulation, 2007, 19(14): 3172-3175.