Comments (1)
Actually, in addition to the previous energy normalization, what is really working for me is to scale the IQ samples between -1 and 1. So that's how this part of my code looks like:
energy = np.sum(np.abs(sampled_vector) ** 2)
sampled_vector = sampled_vector / math.sqrt(energy)
max_val = max(max(np.abs(sampled_vector.real)), max(np.abs(sampled_vector.imag)))
sampled_vector = sampled_vector / max_val
And that's the appearance of the signals after both normalization processes:
As far as I know this kind of normalization is pretty common as some models are more sensitive to the scale of the input data than others. Was there any reason not to originally do this in the dataset? Am I missing something?
It'd be great if someone could give further details on the best practices for normalizing this kind of data.
Regards,
from dataset.
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from dataset.