Assuming known stationary signal and noise spectra and additive noise, the Wiener filter is a filter used in signal processing to provide an estimate of a desired or target random process through linear time-invariant (LTI) filtering of an observed noisy process. The mean square error between the intended process and the estimated random process is reduced by the Wiener filter. Fig: Block diagram view of the FIR Wiener filter for discrete series. An input signal x[n] is convolved with the Wiener filter g[n] and the result is compared to a reference signal s[n] to obtain the filtering error e[n]. In the big picture, the signal is attenuated and added with noise, then the signal is passed through a Wiener filter. And the function of the Wiener filter is to minimize the mean square error between the filter output of the received signal and the reference signal by adjusting the Wiener filter tap coefficient.   Description...

Digital Filters + Add Signal Combine Signals Plot Noisy Signal Plot Filtered Signal Reset All Filter Type: Moving Average Filter FIR Lowpass Filter Butterworth Filter

  Adaptive equalizer adjusts its parameters based on the characteristics of the communication channel. It uses adaptive algorithms to continuously estimate and correct for channel distortion, aiming to minimize errors in the received signal. Adaptive equalizers are versatile and effective in varying channel conditions.   MATLAB Code clc; clear; close all; % Parameters N = 100000; % Number of samples filter_order = 10; % Order of the adaptive filter lambda = 0.99; % Forgetting factor for RLS algorithm delta = 1; % Initial value for the inverse correlation matrix SNR_range = -20:1:20; % SNR range in dB ber = zeros(length(SNR_range), 1); % Initialize BER array % Generate a random signal original_signal = randi([0, 1], N, 1) * 2 - 1; % Bipolar signal (-1, 1) % Channel impulse response h = [0.8, 0.5, 0.2]; % Loop over SNR values for snr_idx = 1:length(SNR_range)     SNR = SNR_range(snr_idx); % Current SNR value          % Pass the signal...

  MATLAB Code clc; clear all; close all; % Define filter parameters fs = 1000; % Sampling frequency (Hz) fpass1 = 50; % Lower cutoff frequency (Hz) fpass2 = 200; % Upper cutoff frequency (Hz) filterOrder = 4; % Filter order % Calculate normalized cutoff frequencies nyquistFreq = fs / 2; normFpass1 = fpass1 / nyquistFreq; normFpass2 = fpass2 / nyquistFreq; % Design the bandpass filter filter = designfilt('bandpassiir', ... 'FilterOrder', filterOrder, ... 'HalfPowerFrequency1', normFpass1, ... 'HalfPowerFrequency2', normFpass2, ... 'DesignMethod', 'butter'); % Visualize the filter frequency response fvtool(filter, 'Analysis', 'freq'); % Print filter coefficients disp('Filter Coefficients:'); disp(filter.Coefficients);   Output          Fig 1: Magnitude response and phase response of a filter with the bandpass filter order of 4 In the above figure,  Step 1: Compute Nyquist Frequency Nyquist frequency = f s 2 = 10...

  MATLAB Code   clc; clear all; close all; amplitude = 1; % Amplitude of the sine wave frequency = 10; % Frequency of the sine wave in Hertz duration = 1; % Duration of the signal in seconds Fs = 10000; % Sampling frequency % Time vector t = 0:1/Fs:duration; % Generate the sine wave signal sinusoidal_signal = amplitude * sin(2*pi*frequency*t) + amplitude*2 * cos(2*pi*500*t); % Plot the sine wave signal figure; plot(t, sinusoidal_signal, 'b-', 'LineWidth', 2); title('Sinusoidal Signal'); xlabel('Time (seconds)'); ylabel('Amplitude'); grid on; figure() %demdulated d=sinusoidal_signal; filter=fir1(200, 10/(Fs/2), 'low'); original_t_signal=conv(filter,d); t1=0:1/(length(original_t_signal)-1):1; plot(t1,original_t_signal); title('demodulated signal'); Output         Fig: sin(2*pi*10*t) + 2cos(2*pi*500*t)       After Applying a Low-Pass filter which cut-off frequency is 10Hz,  we get this        Fig:   sin(2*pi*10*t) ...