MTC.m

format compact;
clc;
close all;
clear all;

channel_taps = 16; % number of channel taps present in the FIR filter
desired_noise_variance = 1; % variance of the gaussian noise present in the desired output data
input_noise_variance = 1; % variance of the gaussian noise present in the desired input data

filter_weights = normalize(rand(channel_taps,1)); % initializing normalized random values for the channel taps of FIR filter
weight_update = zeros(channel_taps,1); % inital guess of the filter weights choosen to be all zero vector
step_size = 0.04; % step size or step length

wait_bar = waitbar(0,'Starting processing');
mu_MTC = step_size; % step length of the MCC update method
iteration = 5000; % total number of iterations done
experiment = 1000; % ensemble-average independent runs
sigma_MTC = 1; % selecting value of the sigma for MTC

% selected parameters
mean_square_deviation_main = zeros(iteration,1); % Mean Square Deviation
mean_square_error_main = zeros(iteration,1); % Mean Square Error
excess_mean_square_error_main = zeros(iteration,1); % Excess Mean Square Error

for dummy_var_2 = 1:experiment
    wait_bar_percentage = dummy_var_2/experiment *100;
    wait_bar = waitbar(dummy_var_2/experiment, wait_bar, strcat('Percentage complete.....',string(floor(wait_bar_percentage)),'%'));
    u_i = zeros(1,channel_taps); % input vector
    mean_square_deviation = zeros(iteration,1); % Mean Square Deviation
    mean_square_error = zeros(iteration,1); % Mean Square Error
    excess_mean_square_error = zeros(iteration,1); % Excess Mean Square Error
    w_MTC = weight_update; % setting the weight update vector equal to the initial guess which is all zero vector
    for dummy_var = 1:iteration
        new_tx_symbol = abs(normrnd(0,1)); % Gaussian random numbers with mean 0 and variance 1
        tx_symbol(dummy_var) = new_tx_symbol + randn*sqrt(input_noise_variance);
        u_i = [new_tx_symbol u_i(1:end-1)]; % generate regressor/input signal (u_i - a row vector of size 1xM)
        d_i= u_i*filter_weights + randn*sqrt(desired_noise_variance); % generate noisy version of channel output as received symbol
        beta = desired_noise_variance/input_noise_variance;  % ratio of the noise variance of desired and input
        w_tilde_MTC = beta-w_MTC; % modified augumented weight vector
        e_i_MTC = d_i - u_i*w_MTC; % finding error between desired output and filter output to update adaptive filter
        if dummy_var<100
            a_i = (norm(w_tilde_MTC)^2 * e_i_MTC * u_i' + e_i_MTC^2*w_MTC) / norm(w_tilde_MTC)^2; % update of the weight vector using GD-TLS
        else
            a_i = (1/sigma_MTC^2)*exp(-e_i_MTC^2/(2*sigma_MTC^2*norm(w_tilde_MTC)^2))*(norm(w_tilde_MTC)^2 * e_i_MTC * u_i' + e_i_MTC^2*w_MTC) / norm(w_tilde_MTC)^2; % update of the weight vector using MTC
        end
        
        w_MTC = w_MTC + mu_MTC*a_i; % updating the adaptive filter after finding the error using MCC algorithm
        
        %calculation of the parameter
        mean_square_deviation(dummy_var) = norm(w_MTC-filter_weights)^2; % mean square deviation calculation
        excess_mean_square_error(dummy_var) = norm(e_i_MTC)^2; % Excess Mean Square Error calculation
        mean_square_error(dummy_var) = excess_mean_square_error(dummy_var)+ desired_noise_variance; % Mean Square Error Calculation
    end

    mean_square_deviation = mean_square_deviation/max(mean_square_deviation);
    excess_mean_square_error = excess_mean_square_error/max(excess_mean_square_error);
    mean_square_error = mean_square_error/max(mean_square_error);
% 
    mean_square_deviation_main = mean_square_deviation_main + mean_square_deviation;
    mean_square_error_main = mean_square_error_main + mean_square_error;
    excess_mean_square_error_main = excess_mean_square_error_main + excess_mean_square_error;
end

mean_square_deviation = mean_square_deviation_main/experiment;
mean_square_error = mean_square_error_main/experiment;
excess_mean_square_error = excess_mean_square_error_main/experiment;
 
% Plot for Mean Square Devivation Curve
figure;
plot(10*log10(mean_square_deviation),'linewidth',1);
xlabel('iteration')
ylabel('Mean Square Deviation (dB)');
legend('MTC')


% Plot for Excess Mean Square Error Curve
figure;
plot(10*log10(excess_mean_square_error),'linewidth',1);
xlabel('iteration')
ylabel('Excess Mean Square Error (dB)');
legend('MTC')


% Plot for Mean Square Error Curve
figure;
plot(10*log10(mean_square_error),'linewidth',1);
xlabel('iteration')
ylabel('Mean Square Error (dB)');
legend('MTC')



close(wait_bar);