Upsampling and Downsampling

For the DTFT, we proved in Chapter 2 (p. p. [*]) the stretch theorem (repeat theorem) which relates upsampling (``stretch'') to spectral copies (``images'') in the DTFT context; this is the discrete-time counterpart of the scaling theorem for continuous-time Fourier transformsB.4). Also, §2.3.12 discusses the downsampling theorem (aliasing theorem) for DTFTs which relates downsampling to aliasing for discrete-time signals. In this section, we review the main results.

Upsampling (Stretch) Operator

Figure: Upsampling by a factor of $ N$ : $ y \isdeftext
\hbox{\sc Stretch}_N(x)$ .

Figure 11.1 shows the graphical symbol for a digital upsampler by the factor $ N$ . To upsample by the integer factor $ N$ , we simply insert $ N-1$ zeros between $ x(n)$ and $ x(n+1)$ for all $ n$ . In other words, the upsampler implements the stretch operator defined in §2.3.9:

y(n) &=& \hbox{\sc Stretch}_{N,n}(x)\\
&\isdef & \left\{\begin{array}{ll}
x(n/N), & \frac{n}{N}\in{\bf Z} \\ [5pt]
0, & \hbox{otherwise}. \\
\end{array} \right.

In the frequency domain, we have, by the stretch (repeat) theorem for DTFTs:

Y(z) &=& \hbox{\sc Repeat}_{N,z}(X)\\
&\isdef & X(z^N), \quad z\in{\bf C}.

Plugging in $ z=e^{j\omega}$ , we see that the spectrum on $ [-\pi,\pi)$ contracts by the factor $ N$ , and $ N$ images appear around the unit circle. For $ N=2$ , this is depicted in Fig.11.2.

Figure: Illustration of $ \hbox {\sc Repeat}_2$ in the frequency domain.

Downsampling (Decimation) Operator

Figure: Downsampling by $ N$ .

Figure 11.3 shows the symbol for downsampling by the factor $ N$ . The downsampler selects every $ N$ th sample and discards the rest:

y(n) &=& \hbox{\sc Downsample}_{N,n}(x)\\
&\isdef & x(Nn), \quad n\in{\bf Z}

In the frequency domain, we have

Y(z) &=& \hbox{\sc Alias}_{N,z}(X)\\
&\isdef &
\frac{1}{N} \sum_{m=0}^{N-1} X\left(z^\frac{1}{N}e^{-jm\frac{2\pi}{N}} \right),
\quad z\in{\bf C}.

Thus, the frequency axis is expanded by the factor $ N$ , wrapping $ N$ times around the unit circle, adding to itself $ N$ times. For $ N=2$ , two partial spectra are summed, as indicated in Fig.11.4.

Figure: Illustration of $ \hbox {\sc Alias}_2$ in the frequency domain.

Using the common twiddle factor notation

$\displaystyle W_N \isdefs e^{-j2\pi/N},$ (12.1)

the aliasing expression can be written as

$\displaystyle Y(z) \eqsp \frac{1}{N} \sum_{m=0}^{N-1} X(W_N^m z^{1/N}).

Example: Downsampling by 2

For $ N=2$ , downsampling by 2 can be expressed as $ y(n) = x(2n)$ , so that (since $ W_2\isdef e^{-j2\pi/2}=-1$ )

Y(z) &=& \frac{1}{2}\left[X\left(W^0_2 z^{1/2}\right) + X\left(W^1_2 z^{1/2}\right)\right] \\ [5pt]
&=& \frac{1}{2}\left[X\left(e^{-j2\pi 0/2} z^{1/2}\right) + X\left(e^{-j2\pi 1/2}z^{1/2}\right)\right] \\ [5pt]
&=& \frac{1}{2}\left[X\left(z^{1/2}\right) + X\left(-z^{1/2}\right)\right] \\ [5pt]
&=& \frac{1}{2}\left[\hbox{\sc Stretch}_2(X) + \hbox{\sc Stretch}_2\left(\hbox{\sc Shift}_\pi(X)\right)\right].

Example: Upsampling by 2

For $ N=2$ , upsampling (stretching) by 2 can be expressed as
$ y=[x_0,0,x_1,0,\ldots]$ , so that

$\displaystyle Y(z) \eqsp X(z^2) \eqsp \hbox{\sc Repeat}_2(X),$ (12.2)

as discussed more fully in §2.3.11.

Filtering and Downsampling

Because downsampling by $ N$ causes aliasing of any frequencies in the original signal above $ \vert\omega\vert > \pi/N$ , the input signal may need to be first lowpass-filtered to prevent this aliasing, as shown in Fig.11.5.

Figure 11.5: Lowpass filtering followed by downsampling.

Suppose we implement such an anti-aliasing lowpass filter $ h(n)$ as an FIR filter of length $ M\le N$ with cut-off frequency $ \pi/N$ .12.1 This is drawn in direct form in Fig.11.6.

Figure 11.6: Direct-form implementation of an FIR anti-aliasing lowpass filter followed by a downsampler.

Figure 11.7: FIR lowpass filter with downsampler commuted inside the direct-form filter.

We do not need $ N-1$ out of every $ N$ filter output samples due to the $ N$ :$ 1$ downsampler. To realize this savings, we can commute the downsampler through the adders inside the FIR filter to obtain the result shown in Fig.11.7. The multipliers are now running at $ 1/N$ times the sampling frequency of the input signal $ x(n)$ . This reduces the computation requirements by a factor of $ 1/N$ . The downsampler outputs in Fig.11.7 are called polyphase signals. The overall system is a summed polyphase filter bank in which each ``subphase filter'' is a constant scale factor $ h(m)$ . As we will see, more general subphase filters can be used to implement time-domain aliasing as needed for Portnoff windows (§9.7).

We may describe the polyphase processing in the anti-aliasing filter of Fig.11.7 as follows:

  • Subphase signal 0

    $\displaystyle x(nN)\left\vert _{n=0}^{\infty}\right. \eqsp [x_0,x_N,x_{2N},\ldots]$ (12.3)

    is scaled by $ h(0)$ .

  • Subphase signal 1

    $\displaystyle x(nN-1)\left\vert _{n=0}^{\infty}\right.\eqsp [x_{-1},x_{N-1},x_{2N-1},\ldots]$ (12.4)

    is scaled by $ h(1)$ ,

  • $ \cdots$

  • Subphase signal $ M-1$

    $\displaystyle x(nN-m)\left\vert _{n=0}^{\infty}\right.\eqsp [x_{-m},x_{N-m},x_{2N-m},\ldots]

    is scaled by $ h(M-1)$ .
These scaled subphase signals are finally summed to form the output signal shown in Fig.11.7

$\displaystyle y(n) \eqsp \sum_{m=0}^{M-1} h(m)\, x(nN-m),$ (12.5)

which we recognize as a direct-form-convolution implementation of a length $ M$ FIR filter $ h$ , with its output downsampled by the factor $ N$ .

The summed polyphase signals of Fig.11.7 can be interpreted as ``serial to parallel conversion'' from an ``interleaved'' stream of scalar samples $ x(n)$ to a ``deinterleaved'' sequence of buffers (each length $ M$ ) every $ N$ samples, followed by an inner product of each buffer with $ h = [h(0),\ldots,h(M-1)]$ . The same operation may be visualized as a deinterleaving through variable gains into a running sum, as shown in Fig.11.8.

Figure: Demultiplex-and-sum interpretation of the polyphase signal sum of Fig.11.7.

Next Section:
Polyphase Decomposition
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