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This module is included inLens: Signal Processing
By: Daniel McKennaAs a part of collection: "Fundamentals of Signal Processing"

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# Proof

Module by: Anders Gjendemsjø. E-mail the author

Summary: Proof of Shannon's sampling theorem

## Sampling theorem:

In order to recover the signal xtxt from it's samples exactly, it is necessary to sample xtxt at a rate greater than twice it's highest frequency component.

## Introduction

As mentioned earlier, sampling is the necessary fundament when we want to apply digital signal processing on analog signals.

Here we present the proof of the sampling theorem. The proof is divided in two. First we find an expression for the spectrum of the signal resulting from sampling the original signal xtxt. Next we show that the signal xtxt can be recovered from the samples. Often it is easier using the frequency domain when carrying out a proof, and this is also the case here.

### Key points in the proof

• We find an equation for the spectrum of the sampled signal
• We find a simple method to reconstruct the original signal
• The sampled signal has a periodic spectrum...
• ...and the period is 2×πFs 2 π Fs

## Proof part 1 - Spectral considerations

By sampling xtxt every TsTs second we obtain xsnxsn. The inverse fourier transform of this time discrete signal is

xsn=12πππXseiωeiωndω xs n 1 2 π ω π Xs ω ω n
(1)
For convenience we express the equation in terms of the real angular frequency ΩΩ using ω=ΩTs ω Ω Ts . We then obtain
xsn=Ts2ππTsπTsXseiΩTseiΩTsndΩ xs n Ts 2 Ω π Ts π Ts Xs Ω Ts Ω Ts n
(2)
The inverse fourier transform of a continuous signal is
xt=12πXiΩeiΩtdΩ x t 1 2 Ω X Ω Ω t
(3)
From this equation we find an expression for x (nTs) x n Ts
xnTs=12πXiΩeiΩnTsd Ω x n Ts 1 2 Ω X Ω Ω n Ts
(4)
To account for the difference in region of integration we split the integration in Equation 4 into subintervals of length 2πTs 2 π Ts and then take the sum over the resulting integrals to obtain the complete area.
xnTs=12πk=(2k1)πTs(2k+1)πTsXiΩeiΩnTsdΩ x n Ts 1 2 π k Ω 2 k 1 Ts 2 k 1 Ts X Ω Ω n Ts
(5)
Then we change the integration variable, setting Ω=η+2×πkTs Ω η 2 π k Ts
xnTs=12πk=πTsπTsXi(η+2×πkTs)ei(η+2×πkTs)nTsdη x n Ts 1 2 π k η Ts π Ts X η 2 π k Ts η 2 π k Ts n Ts
(6)
We obtain the final form by observing that ei2×πkn=1 2 π k n 1 , reinserting η=ΩηΩ and multiplying by TsTs Ts Ts
xnTs=Ts2ππTsπTsk=1TsX(i(Ω+2×πkTs))eiΩnTsdΩ x n Ts Ts 2 π Ω π Ts π Ts k 1 Ts X Ω 2 π k Ts Ω n Ts
(7)
To make xsn=xnTs xs n x n Ts for all values of nn, the integrands in Equation 2 and Equation 7 have to agreee, that is
XseiΩTs=1Tsk=X(i(Ω+2πkTs)) Xs Ω Ts 1 Ts k X Ω 2 k Ts
(8)
This is a central result. We see that the digital spectrum consists of a sum of shifted versions of the original, analog spectrum. Observe the periodicity!

We can also express this relation in terms of the digital angular frequency ω=ΩTs ω Ω Ts

Xseiω=1Tsk=X(iω+2×πkTs) Xs ω 1 Ts k X ω 2 π k Ts
(9)
This concludes the first part of the proof. Now we want to find a reconstruction formula, so that we can recover xtxt from xsnxsn.

## Proof part II - Signal reconstruction

For a bandlimited signal the inverse fourier transform is

xt=12ππTsπTsXiΩeiΩtdΩ x t 1 2 Ω Ts Ts X Ω Ω t
(10)
In the interval we are integrating we have: XseiΩTs=XiΩTs Xs Ω Ts X Ω Ts . Substituting this relation into Equation 10 we get
xt=Ts2ππTsπTsXseiΩTseiΩtdΩ x t Ts 2 Ω Ts Ts Xs Ω Ts Ω t
(11)
Using the DTFT relation for XseiΩTs Xs Ω Ts we have
xt=Ts2ππTsπTsn=xsne(iΩnTs)eiΩtdΩ x t Ts 2 Ω Ts Ts n xs n Ω n Ts Ω t
(12)
Interchanging integration and summation (under the assumption of convergence) leads to
xt=Ts2πn=xsnπTsπTseiΩ(tnTs)dΩ x t Ts 2 n xs n Ω Ts Ts Ω t n Ts
(13)
Finally we perform the integration and arrive at the important reconstruction formula
xt=n=xsnsinπTs(tnTs)πTs(tnTs) x t n xs n Ts t n Ts Ts t n Ts
(14)
(Thanks to R.Loos for pointing out an error in the proof.)

## Summary

### spectrum sampled signal:

XseiΩTs=1Tsk=X(i(Ω+2πkTs)) Xs Ω Ts 1 Ts k X Ω 2 k Ts

### Reconstruction formula:

xt=n=xsnsinπTs(tnTs)πTs(tnTs) x t n xs n Ts t n Ts Ts t n Ts

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