Furthermore you already calculated error between them. As I understood x is an input for our model and y is an output. To get this we have to find error between input and output. J(k,n) = e * e' % Instantaneous square errorįirst of all when using some fitting method it is a good practice to use RMS error. W = W + eta * e * U % Weight update rule of LMS X = awgn(r, SNRr) % Noisy Signal after channel (given/input)Įpoch = 10 % Number of epochs (training repetation) R = filter(h,1,d) % Signal after passing through channel %% Channel and noise levelīits = 2 % Number of bits for modulation (2-bit for Binary modulation)ĭ = real(pskmod(data,Bits)) % BPSK Modulated signal (desired/output) ![]() I am confused since the measured output x is noisy and the model output y*w is noise-free. Should the graph be plotted using x and y*w? But x is noisy. u is the input to the equalizer, x is the noisy received signal, y is the output of the equalizer, w is the equalizer weights. ![]() ![]() Can somebody please help what is the proper way to plot the comparison between the model and the measured data? If the estimates are close to true, then the curves should be very close to each other. I am using the Least Mean Square algorithm as the equalization technique. Basically, I want to compare the measured output and the model output. To do this, prediction error plot is often used. I want to determine how well the estimated model fits to the future new data.
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