clc;
clear;
close all;
% -----------------------
% PARAMETERS AND SIGNALS
% -----------------------
n_samples = 500; % Number of samples
t = 0:n_samples-1; % Time vector
% Desired signal (sinusoidal)
desired_signal = sin(2*pi*0.01*t);
% Additive white Gaussian noise (AWGN)
noise = 0.5 * randn(1, n_samples);
% Noisy signal
noisy_signal = desired_signal + noise;
% Channel variability simulation (fading)
fading = 0.5 + 0.5 * sin(2*pi*0.005*t); % Simulate channel gain variability
channel_signal = fading .* noisy_signal;
% -----------------------
% NOISE AND INTERFERENCE MITIGATION (LMS FILTERING)
% -----------------------
filter_order = 8; % Order of adaptive filter
mu = 0.01; % Learning rate for LMS
weights = zeros(filter_order, 1); % Initialize weights
filtered_signal = zeros(1, n_samples); % Initialize filtered signal
for i = filter_order:n_samples
x = noisy_signal(i:-1:i-filter_order+1); % Input vector
filtered_signal(i) = weights' * x'; % Filter output
error = desired_signal(i) - filtered_signal(i); % Error signal
weights = weights + 2 * mu * error * x'; % Update weights
end
% -----------------------
% ADAPTIVE MODULATION (BASED ON SNR)
% -----------------------
snr = 10 * log10(var(channel_signal) / var(noise)); % Compute SNR
if snr < 5
modulation_scheme = 'BPSK'; % Low SNR
elseif snr < 15
modulation_scheme = 'QPSK'; % Medium SNR
elseif snr < 25
modulation_scheme = '16-QAM'; % High SNR
else
modulation_scheme = '64-QAM'; % Very High SNR
end
disp(['Selected Modulation Scheme: ', modulation_scheme]);
% -----------------------
% SPECTRAL EFFICIENCY (OFDM)
% -----------------------
n_subcarriers = 64; % Number of OFDM subcarriers
modulation_order = 16; % 16-QAM
data = randi([0 modulation_order-1], n_subcarriers, n_samples); % Random data
% OFDM Modulation
modulated_data = qammod(data(:), modulation_order, 'UnitAveragePower', true);
ofdm_symbols = ifft(modulated_data, n_subcarriers);
% Adding AWGN to OFDM symbols
noisy_ofdm = awgn(ofdm_symbols, snr, 'measured');
% OFDM Demodulation
demodulated_data = fft(noisy_ofdm, n_subcarriers);
recovered_data = qamdemod(demodulated_data, modulation_order);
% -----------------------
% REAL-TIME ADAPTATION (KALMAN FILTER)
% -----------------------
x_estimated = zeros(1, n_samples); % Initialize estimated signal
P = 1; % Initial error covariance
Q = 0.01; % Process noise covariance
R = 0.5; % Measurement noise covariance
x = 0; % Initial state
for i = 1:n_samples
% Prediction step
P = P + Q; % Update error covariance
% Measurement update
K = P / (P + R); % Kalman gain
x = x + K * (noisy_signal(i) - x); % Update state estimate
P = (1 - K) * P; % Update error covariance
% Store the result
x_estimated(i) = x;
end
% -----------------------
% PLOT RESULTS
% -----------------------
figure;
% Original, Noisy, and Filtered signals
subplot(3, 1, 1);
plot(t, desired_signal, 'g', 'LineWidth', 1.5); hold on;
plot(t, noisy_signal, 'r', 'LineWidth', 1);
plot(t, filtered_signal, 'b', 'LineWidth', 1);
legend('Desired Signal', 'Noisy Signal', 'Filtered Signal');
title('LMS Filtering');
xlabel('Samples'); ylabel('Amplitude'); grid on;
% Fading channel effect
subplot(3, 1, 2);
plot(t, fading, 'k', 'LineWidth', 1.5);
title('Simulated Fading Channel');
xlabel('Samples'); ylabel('Channel Gain'); grid on;
% Kalman Filter adaptation
subplot(3, 1, 3);
plot(t, noisy_signal, 'r', 'LineWidth', 1); hold on;
plot(t, x_estimated, 'b', 'LineWidth', 1.5);
legend('Noisy Signal', 'Kalman Filter Output');
title('Kalman Filtering for Real-Time Adaptation');
xlabel('Samples'); ylabel('Amplitude'); grid on;
disp('Program completed successfully!');
what was wrong in this code
