## Sampling Theorem

Let denote any continuous-time signal having a *continuous* Fourier transform

^{D.3}

*Proof: *From the continuous-time aliasing theorem (§D.2), we
have that the discrete-time spectrum
can be written in
terms of the continuous-time spectrum
as

To reconstruct from its samples , we may simply take the inverse Fourier transform of the zero-extended DTFT, because

By expanding as the DTFT of the samples , the formula for reconstructing as a superposition of weighted sinc functions is obtained (depicted in Fig.D.1):

where we defined

or

We have shown that when is bandlimited to less than half the
sampling rate, the IFT of the zero-extended DTFT of its samples
gives back the original continuous-time signal .
This completes the proof of the
sampling theorem.

Conversely, if can be reconstructed from its samples , it must be true that is bandlimited to , since a sampled signal only supports frequencies up to (see §D.4 below). While a real digital signal may have energy at half the sampling rate (frequency ), the phase is constrained to be either 0 or there, which is why this frequency had to be excluded from the sampling theorem.

A one-line summary of the essence of the sampling-theorem proof is

The sampling theorem is easier to show when applied to sampling-rate
conversion in discrete-time, *i.e.*, when simple downsampling of a
discrete time signal is being used to reduce the sampling rate by an
integer factor. In analogy with the continuous-time aliasing theorem
of §D.2, the downsampling theorem (§7.4.11)
states that downsampling a digital signal by an integer factor
produces a digital signal whose spectrum can be calculated by
partitioning the original spectrum into equal blocks and then
summing (aliasing) those blocks. If only one of the blocks is
nonzero, then the original signal at the higher sampling rate is
exactly recoverable.

**Next Section:**

Appendix: Frequencies Representable by a Geometric Sequence

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Aliasing of Sampled Signals