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This example shows how to establish an equivalence between linear and circular convolution.

Linear and circular convolution are fundamentally different
operations. However, there are conditions under which linear and circular
convolution are equivalent. Establishing this equivalence has important
implications. For two vectors, *x* and *y*,
the circular convolution is equal to the inverse discrete Fourier
transform (DFT) of the product of the vectors' DFTs. Knowing
the conditions under which linear and circular convolution are equivalent
allows you to use the DFT to efficiently compute linear convolutions.

The linear convolution of an N-point vector, *x*,
and a L-point vector, *y*, has length N+L-1.

For the circular convolution of *x* and *y* to
be equivalent, you must pad the vectors with zeros to length at least
N+L-1 before you take the DFT. After you invert the product of the
DFTs, retain only the first N+L-1 elements.

Create two vectors, *x* and *y*,
and compute the linear convolution of the two vectors.

x = [2 1 2 1]; y = [1 2 3]; clin = conv(x,y);

The output has length 4+3–1.

Pad both vectors with zeros to length 4+3–1. Obtain the DFT of both vectors, multiply the DFTs, and obtain the inverse DFT of the product.

xpad = [x zeros(1,6-length(x))]; ypad = [y zeros(1,6-length(y))]; ccirc = ifft(fft(xpad).*fft(ypad));

The circular convolution of the zero-padded vectors, `xpad` and `ypad`,
is equivalent to the linear convolution of `x` and `y`.
You retain all the elements of `ccirc` because the
output has length 4+3–1.

Plot the output of linear convolution and the inverse of the DFT product to show the equivalence.

subplot(211) stem(clin,'markerfacecolor',[0 0 1]); title('Linear Convolution of x and y'); set(gca,'ylim',[0 11]); subplot(212) stem(ccirc,'markerfacecolor',[0 0 1]); set(gca,'ylim',[0 11]); title('Circular Convolution of xpad and ypad');

Pad the vectors to length 12 and obtain the circular convolution using the inverse DFT of the product of the DFTs. Retain only the first 4+3–1 elements to produce an equivalent result to linear convolution.

N = length(x)+length(y)-1; xpad = [x zeros(1,12-length(x))]; ypad = [y zeros(1,12-length(y))]; ccirc = ifft(fft(xpad).*fft(ypad)); ccirc = ccirc(1:N);

The Signal Processing Toolbox™ software has a function, `cconv`,
that returns the circular convolution of two vectors. You can obtain
the linear convolution of `x` and `y` using
circular convolution with the following code.

ccirc2 = cconv(x,y,6);

`cconv` internally uses the same DFT-based
procedure illustrated in the previous example.

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