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Fast Fourier Transform

Explore the Fast Fourier Transform to understand how it dramatically reduces the computational complexity of the Discrete Fourier Transform. Learn why FFT is essential for efficient spectrum analysis of digital signals and how it outperforms traditional DFT methods in practical applications.

We have learned that the DFT computes the spectrum of a length NN signal at NN equally spaced frequencies. The exact expression is given as:

X[k]=n=0N1x[n]ej2πkNnX[k] = \sum_{n=0}^{N-1}x[n]e^{-j2\pi\frac{k}{N}n}

When implemented in real systems, that is a lot of operations to perform for any digital signal processor, which we’ll explore in the next section.

Complexity of the DFT

If x[n]x[n] is real, we have two complex multiplications for each frequency—one with the real and the other with the imaginary part of the analysis sinusoid. In total, these are 2N2N multiplications for each kk. Then, adding NN complex numbers requires 2(N1)2(N-1) ...