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Conventional AM and narrowband PM signals look quite similar: $$x_{AM}(t) = A \cos(\omega_c t) + m(t) \cos(\omega_c t)$$ $$x_{NBPM}(t) = A \cos(\omega_c t) - A k_p m(t) \sin(\omega_c t)$$ And based on this, their spectrum also look quite similar. However, there's an important distinction between them: AM modulation changes (modulates) the amplitude of the ...

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It's almost a matter of philosophy, i.e., difficult to argue hard facts. On the one hand all the features you mention can be extracted from the raw signals. So in theory the network should be able to learn how to do that if they provide meaningful information for the task at hand. This is what part of the ML community is claiming: feature engineering is dead,...

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You've made an error in this statement: "I know by sampling in time domain we get shifted replicas of the original spectrum which is two delta functions at $f_0$ and $−f_0$ but how this corresponds to $f_s$ and aliasing at $f_0+kf_s$?" The two delta functions at $-f_0$ and $f_0$ are the result of taking the Fourier transform of a sinusoid $x(t)$: ...

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One reason is that higher frequencies are envisioned. With higher frequencies, the path loss grows (cf. Friis equation). Also, the wavelength is reduced and thus, $\lambda/2$ radiators start becoming quite small. The power you can radiate from a small aperture is limited as well so overall our power budget suffers. The only way out is directivity (cf. Friis ...

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DFT vs DTFT, Fourier Transform The problem appears rooted in viewing DFT as a 'special case' of the continuous Fourier Transform, and of its input as some signal with legitimate frequency contents. That's a fallacy. The DFT is a standalone mathematical transformation which does not condition itself upon anything but the input being finitely long, and having ...

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