Frequency Shifting and Modulation

Learn how a shift in frequency is implemented in discrete-time processing.

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Given a signal, a shift in time results in a product between the original signal spectrum and a frequency-domain complex sinusoid. This has a counterpart in the frequency domain, as follows.

Complex signals

If we multiply a signal x[n]x[n] with a complex sinusoid in the time domain, we get a shift in the signal spectrum. Let’s call this new signal y[n]y[n].

y[n]=x[n]ej2πk0Nny[n]=x[n]\cdot e^{j2\pi\frac{k_0}{N}n}

From the DFT definition, we have:

Y[k]=n=0N1(x[n]ej2πk0Nn)ej2πkNn=n=0N1x[n]ej2πkk0Nn=X[kk0]\begin{align*} Y[k]=&\sum_{n=0}^{N-1}\left(x[n]e^{j2\pi\frac{k_0}{N}n}\right)e^{-j2\pi\frac{k}{N}n}\\ =&\sum_{n=0}^{N-1}x[n]e^{-j2\pi\frac{k-k_0}{N}n}\\ =&X[k-k_0] \end{align*}

Real signals

What happens when, instead of a complex sinusoid, we multiply that signal with a real sinusoid? Euler’s identity is of help here.

cos(ωn)=ejωn+ejωn2\cos( \omega n) = \frac{e^{j\omega n}+e^{-j\omega n}}{2}

Clearly, x[n]cos(ωn)x[n]\cos (\omega n) would now result in two shifts, one at +ω+\omega (the positive part of the spectrum) and the other at ω-\omega (the negative part of the spectrum). Let’s verify this by executing the following code:

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