AnotherPenName
Newbie level 1
Hello all,
I am trying to create the variable step-size NLMS algorithm in the paper “A New Variable Step Size NLMS Algorithm and Its Performance Analysis” by Hsu-Chang Huang and Junghsi Lee, but the performance is only comparable to the standard NLMS algorithm. I though it would performs far better then the standard NLMS algorithm. I’ve checked and double checked the code, but I don’t see anything wrong with it. Please take a look at the code below and let me know if I did anything wrong.
Thanks,
-Andrew
A picture of the algorithm can be found here: https://imageshack.us/photo/my-images/855/vsmlms.png/
View attachment VSMLMS.bmp
I am trying to create the variable step-size NLMS algorithm in the paper “A New Variable Step Size NLMS Algorithm and Its Performance Analysis” by Hsu-Chang Huang and Junghsi Lee, but the performance is only comparable to the standard NLMS algorithm. I though it would performs far better then the standard NLMS algorithm. I’ve checked and double checked the code, but I don’t see anything wrong with it. Please take a look at the code below and let me know if I did anything wrong.
Thanks,
-Andrew
A picture of the algorithm can be found here: https://imageshack.us/photo/my-images/855/vsmlms.png/
View attachment VSMLMS.bmp
Code:
%=================================================
clear all; close all; clc;
k = 5000; % number of samples
run = 100;
tap = 64; % number of filter weights
sigma_n2 = 0.01; % noise power
sigma_x2 = 1; % signal power
unknown_system = fircband(tap-1,[0 0.4 0.5 1], [1 1 0 0],[1 0.2], {'w' 'c'});
x = sqrt(sigma_x2)*randn(k,1); % Creating the input signal
n = sqrt(sigma_n2)*randn(1,k); % noise
d = zeros(k,1); % desired signal = unknown_system' * x + n
X = zeros(tap,1);
for p = 1:k
X = [x(p,1); X(1:(tap-1),1)]; % input signal (tapped delay line)
d(p) = (unknown_system*X(:,1)) + n(p); % desired signal
end
xk = [zeros(1,tap-1) x'];
w0 = zeros(tap,1);
weight(:,1) = reshape(w0,length(w0),1);
mu = 1;
alpha = 0.998;
beta = 15;
epsilon = 0.01;
sigma2_e = 0;
sigma2_x = 0;
RexHat = 0;
for i = 1:k;
Xk(1:tap,1) = xk(i+(tap-1):-1:i);
Yk(i) = weight(:,i)' * Xk;
e(i) = d(i) - Yk(i);
sigma2_e = alpha*sigma2_e + (1-alpha)*e(i)^2; % Estimate MSE
sigma2_x = alpha*sigma2_x + (1-alpha)*mean(Xk)^2; % Estimate input power
RexHat = alpha*RexHat + (1-alpha)*Xk*e(i);
sigma2_v = sigma2_e - (1/sigma2_x)*(RexHat'*RexHat);
mu = alpha*mu + (1-alpha)*sigma2_e/(beta*sigma2_v);
weight(:,i+1) = weight(:,i) + (mu/(epsilon+Xk'*Xk))*conj(e(i))*Xk;
end
plot(1:length(e),20*log10(e.^2));
Last edited:
