Let denote any continuous-time signal having a continuous Fourier transform
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
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
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.
Appendix: Frequencies Representable by a Geometric Sequence
Aliasing of Sampled Signals