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Let us take a function <math>x(t)</math>
 
Let us take a function <math>x(t)</math>
  
[[Image:sampling_rate_conv.jpg|center]]
+
[[Image:sampling_rate_conv.jpg]]
  
 
We wish to covert a signal sampled at <math>T_1</math> to one sampled at <math>T_2</math> without having to reconstruct the original <math>x(t)</math> and then resampling at a new rate.
 
We wish to covert a signal sampled at <math>T_1</math> to one sampled at <math>T_2</math> without having to reconstruct the original <math>x(t)</math> and then resampling at a new rate.
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[[Image:Xd_w.jpg|center]]
 
[[Image:Xd_w.jpg|center]]
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We see that the scaled function is no longer periodic with period <math>2\pi</math>.
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Now, consider frequency shifting the signal so that we have <math>\frac{1}{D}X_1(\frac{\omega-2\pi}{D})</math>.
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[[Image:Xd2pi_w.jpg]]

Revision as of 20:50, 22 September 2009

Sample Rate Conversion

Let us take a function $ x(t) $

Sampling rate conv.jpg

We wish to covert a signal sampled at $ T_1 $ to one sampled at $ T_2 $ without having to reconstruct the original $ x(t) $ and then resampling at a new rate.

There are two cases here.

1. $ T_2 $ is a multiple of $ T_1 \to $ conversion can be accomplished by down-sampling

2. $ T_2 $ is a divider of $ T_1 \to $ conversion can be accomplished by up-sampling followed by LPF

Case 1 - $ T_2 $ is a multiple of $ T_1 $

We are trying to accomplish the following -

$ x_1[n] \longrightarrow D = \frac{T_2}{T_1} \bigg\Downarrow \longrightarrow x_2[n] $

We know that

$ x_2[n] = x_1[Dn] \quad \mbox{where } D = \frac{T_2}{T_1} $

as this is the same as doing $ x_2[n] = x(T_2n) $

This, in Fourier Domain becomes

$ F(x_2[n]) = F(x_1[Dn]) $

$ X_2(\omega) = \sum_{n=-\infty}^{\infty} x_1[Dn] e^{-j \omega n} $

let $ m = Dn $

$ X_2(\omega) = \sum_{m=-\infty}^{\infty} x_1[m] e^{-j \omega \frac{m}{D}}\quad \mbox{where }m\mbox{ is a multiple of }D $

Now, we can introduce a function $ s_D[m] $ such that

$ s_D[m] = \begin{cases} 1, & \mbox{if }m\mbox{ multiple of }D\\ 0, & \mbox{else } \end{cases} $

The Fourier series of this function can be represented as

$ S_D[m] = \frac{1}{D} \sum_{k = 0}^{D-1} (e^{j \frac{2 \pi}{D} m})^k $

and therefore we get

$ X_2(\omega) = \sum_{m = -\infty}^{\infty} S_D[m] e^{-j \omega \frac{m}{D}} $

$ X_2(\omega) = \sum_{m = -\infty}^{\infty} \frac{1}{D}\sum_{k = 0}^{D-1} e^{j k \frac{2 \pi}{D} m} x_1[m] e^{-j \omega \frac{m}{D}} $

$ X_2(\omega) = \frac{1}{D} \sum_{k=0}^{D-1} \sum_{m=-\infty}^{\infty} x_1[m] e^{-j m (\frac{\omega - 2 \pi k}{D})} $

And since $ \sum_{m=-\infty}^{\infty} x_1[m] e^{-j m (\frac{\omega - 2 \pi k}{D})} = X_1(\frac{\omega - 2 \pi k}{D}) $

Therefore,

$ X_2(\omega) = \frac{1}{D} \sum_{k=0}^{D-1} X_1(\frac{\omega - 2 \pi k}{D}) $

Let us assume the following plot for $ X_1(\omega) $. We will also assume that the Nyquist condition is satisfied, ie. $ f_{max} < \frac{1}{2T_1} $.

X w.jpg

Now, on scaling we have the following plot for $ \frac{1}{D} X_1(\frac{\omega}{D}) $

Xd w.jpg

We see that the scaled function is no longer periodic with period $ 2\pi $.

Now, consider frequency shifting the signal so that we have $ \frac{1}{D}X_1(\frac{\omega-2\pi}{D}) $.

Xd2pi w.jpg

Alumni Liaison

Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett