Signal Representation/Discrete Fourier Transform (DFT)

1. Signal Representation
2. Time and Frequency-Discrete Signal Representation
3. Discrete Fourier Transform (DFT)

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Arguments for the Discrete Realisation of the Fourier Transform

The  Fourier transform  according to the previous description in chapter  Aperiodic Signals – Pulses has an infinitely high selectivity due to the unlimited extension of the integration interval and is therefore an ideal theoretical tool of spectral analysis.

If the spectral components  $X(f)$  of a time function  $x(t)$  are to be determined numerically, the general transformation equations are

$$\begin{align*}X(f) & = \int_{-\infty

}^{+\infty}x(t) \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi f t}\hspace{0.1cm} {\rm d}t\hspace{0.5cm} \Rightarrow\hspace{0.5cm} \text{Transform}\hspace{0.7cm} \Rightarrow\hspace{0.5cm} \text{first  Fourier integral}
\hspace{0.05cm},\\

x(t) & = \int_{-\infty

}^{+\infty}\hspace{-0.15cm}X(f) \cdot {\rm e}^{\hspace{0.05cm}+{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi f t}\hspace{0.1cm} {\rm d}f\hspace{0.35cm} \Rightarrow\hspace{0.5cm}

\text{Back Transform}\hspace{0.4cm} \Rightarrow\hspace{0.5cm} \text{second Fourier integral}

\hspace{0.05cm}\end{align*}$$

unsuitable for two reasons:

• The equations apply exclusively to time-continuous signals. With digital computers or signal processors, however, one can only process time-discrete signals.
• For a numerical evaluation of the two Fourier integrals it is necessary to limit the respective integration interval to a finite value.

$\text{This leads to the following consequence:}$ 

A  continuous signal  must undergo two processes before the numerical determination of its spectral properties, viz.

• that of  sampling  for discretisation, and
• that of  windowing  to limit the integration interval.

In the following, starting from an aperiodic time function  $x(t)$  and the corresponding Fourier spectrum  $X(f)$  a time- and frequency-discrete description suitable for computer processing is developed step by step.

Time Discretisation – Periodisation in the Frequency Domain

The following graphs show uniformly the time domain on the left and the frequency domain on the right. Without limiting generality,  $x(t)$  and  $X(f)$  are each real and Gaussian.

Time Discretisation - Periodisation in the Frequency Domain

According to the chapter  Time Discrete Signal Representation  one can describe the sampling of the time signal  $x(t)$  by multiplying it by a Dirac pulse  $p_{\delta}(t)$ . The result is the time signal sampled at a distance  $T_{\rm A}$ 

$${\rm A}\{x(t)\} = \sum_{\nu = - \infty }^{+\infty} T_{\rm A} \cdot x(\nu \cdot T_{\rm A})\cdot

\delta (t- \nu \cdot T_{\rm A}
)\hspace{0.05cm}.$$

We now transform this sampled signal  $\text{A}\{ x(t)\}$  into the frequency domain. The multiplication of the Dirac pulse  $p_{\delta}(t)$  with  $x(t)$  corresponds in the frequency domain to the convolution of  $P_{\delta}(f)$  with  $X(f)$. The result is the periodised spectrum  $\text{P}\{ X(f)\}$, where  $f_{\rm P}$  indicates the frequency period of the function  $\text{P}\{ X(f)\}$ :

$${\rm A}\{x(t)\} \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} {\rm P}\{X(f)\} = \sum_{\mu = - \infty }^{+\infty}

X (f- \mu \cdot f_{\rm P} )\hspace{0.5cm} {\rm with }\hspace{0.5cm}f_{\rm
P}= {1}/{T_{\rm A}}\hspace{0.05cm}.$$

This relation was also already derived in the chapter  Time Discrete Signal Representation  but with slightly different nomenclature:

• We now denote the sampled signal by  $\text{A}\{ x(t)\}$  instead of  $x_{\rm A}(t)$.
• The  frequency period  is now denoted by  $f_{\rm P} = 1/T_{\rm A}$  instead of  $f_{\rm A} = 1/T_{\rm A}$ .

These nomenclature changes are justified on the following pages.

The graph above shows the functional relationship described here. It should be noted:

• The frequency period  $f_{\rm P}$  has been deliberately chosen to be small here so that the overlap of the spectra to be summed can be clearly seen.
• In practice  $f_{\rm P}$  should be at least twice as large as the largest frequency contained in the signal  $x(t)$  due to the sampling theorem.
• If this is not fulfilled, then  Aliasing  must be reckoned with - see chapter  Possible Errors When Using DFT.

Frequency Discretisation – Periodisation in the Time Domain

The discretisation of  $X(f)$  can also be described by a multiplication with a Dirac comb. The result is the spectrum sampled in the distance  $f_{\rm A}$ :

$${\rm A}\{X(f)\} = X(f) \cdot \sum_{\mu = - \infty }^{+\infty}

f_{\rm A} \cdot \delta (f- \mu \cdot f_{\rm A } ) =  \sum_{\mu = - \infty }^{+\infty}
f_{\rm A} \cdot X(\mu \cdot f_{\rm A } ) \cdot\delta (f- \mu \cdot f_{\rm A } )\hspace{0.05cm}.$$

If one transforms the frequency-dirac comb $($with pulse weights  $f_{\rm A})$  used here into the time domain, one obtains with  $T_{\rm P} = 1/f_{\rm A}$:

$$\sum_{\mu = - \infty }^{+\infty}

f_{\rm A} \cdot \delta (f- \mu \cdot f_{\rm A } ) \hspace{0.2cm}\bullet\!\!-\!\!\!-\!\!\!-\!\!\circ\, \hspace{0.2cm}
\sum_{\nu = - \infty }^{+\infty}
 \delta (t- \nu \cdot T_{\rm P } ) \hspace{0.05cm}.$$

The multiplication with  $X(f)$  corresponds in the time domain to the convolution with  $x(t)$. One obtains the signal  $T_{\rm P}$  periodified in the distance  $\text{P}\{ x(t)\}$:

$${\rm A}\{X(f)\} \hspace{0.2cm}\bullet\!\!-\!\!\!-\!\!\!-\!\!\circ\, \hspace{0.2cm}

{\rm P}\{x(t)\} = x(t) \star \sum_{\nu = - \infty }^{+\infty}
 \delta (t- \nu \cdot T_{\rm P } )= \sum_{\nu = - \infty }^{+\infty}
 x (t- \nu \cdot T_{\rm P } ) \hspace{0.05cm}.$$

Frequency Discretisation – Periodisation in the Time Domain

$\text{Example 1:}$  This correlation is illustrated in the graph:

• Due to the coarse frequency rastering, this example results in a relatively small value for the time period  $T_{\rm P}$ .

• Therefore, the (blue) periodised time signal  $\text{P}\{ x(t)\}$  differs significantly from  $x(t)$.

due to overlaps.

Finite Signal Representation

Finite Signals of the Discrete Fourier Transform (DFT)

One arrives at the so-called  finite signal representation ,

• if both the time function  $x(t)$
• and the spectral function  $X(f)$

are specified exclusively by their sample values.
The graph is to be interpreted as follows:

• In the left picture, drawn in blue, is the function  $\text{A}\{ \text{P}\{ x(t)\}\}$. This results from sampling the periodified time function  $\text{P}\{ x(t)\}$  with equidistant dirac pulses at a distance  $T_{\rm A} = 1/f_{\rm P}$.
• In the right picture the function is drawn in green  $\text{P}\{ \text{A}\{ X(f)\}\}$. This results from periodisation $($with  $f_{\rm P})$  of the sampled spectral function  $\{ \text{A}\{ X(f)\}\}$.
• There is a Fourier correspondence between the blue finite signal (left sketch) and the green finite signal (right sketch), as follows:

$${\rm A}\{{\rm P}\{x(t)\}\} \hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm} {\rm P}\{{\rm A}\{X(f)\}\} \hspace{0.05cm}.$$

• The diraclines of the periodic continuation  $\text{P}\{ \text{A}\{ X(f)\}\}$  of the sampled spectral function, however, only fall into the same frequency grid as those of  $\text{A}\{ X(f)\}$ if the frequency period  $f_{\rm P}$  is an integer multiple  $(N)$  of the frequency sampling interval  $f_{\rm A}$ .
• Therefore, when using the finite signal representation, the following condition must always be fulfilled, where the natural number  $N$  in practice is usually a power of two  (the above graph is based on the value  $N = 8$ ):

$$f_{\rm P} = N \cdot f_{\rm A} \hspace{0.5cm} \Rightarrow\hspace{0.5cm} {1}/{T_{\rm A}}= N \cdot f_{\rm A} \hspace{0.5cm} \Rightarrow\hspace{0.5cm}

N \cdot f_{\rm A}\cdot T_{\rm A} = 1\hspace{0.05cm}.$$

• If the condition  $N \cdot f_{\rm A} \cdot T_{\rm A} = 1$  the order of periodization and sampling is interchangeable. Thus:

$${\rm A}\{{\rm P}\{x(t)\}\} = {\rm P}\{{\rm A}\{x(t)\}\}\hspace{0.2cm}\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet\, \hspace{0.2cm}

{\rm P}\{{\rm A}\{X(f)\}\} = {\rm A}\{{\rm P}\{X(f)\}\}\hspace{0.05cm}.$$

$\text{Conclusion:}$ 

• The time function  $\text{P}\{ \text{A}\{ x(t)\}\}$  has the period  $T_{\rm P} = N \cdot T_{\rm A}$.
• The period in the frequency domain is  $f_{\rm P} = N \cdot f_{\rm A}$.
• For the description of the discretised time and frequency response in each case  $N$  complex numerical values in the form of pulse weights are thus sufficient.

$\text{Example 2:}$  A time-limited (pulse-like) signal  $x(t)$  is present in sampled form, where the distance between two samples  $T_{\rm A} = 1\, {\rm µ s}$  is:

• After a discrete Fourier transform with  $N = 512$  the spectrum  $X(f)$  is in the form of samples spaced  $f_{\rm A} = (N \cdot T_{\rm A})^{-1} \approx 1.953\,\text{kHz} $  before.
• Increasing the DFT–parameter to  $N= 2048$ results in a (four times) finer frequency grid with  $f_{\rm A} \approx 488\,\text{Hz}$.

From the Continuous to the Discrete Fourier Transform

From the conventional first Fourier integral

$$X(f) =\int_{-\infty

}^{+\infty}x(t) \cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi \hspace{0.05cm}\cdot \hspace{0.05cm} f  \hspace{0.05cm}\cdot \hspace{0.05cm}t}\hspace{0.1cm} {\rm d}t$$

arises from discretisation  $(\text{d}t \to T_{\rm A}$,  $t \to \nu \cdot T_{\rm A}$,  $f \to \mu \cdot f_{\rm A}$,  $T_{\rm A} \cdot f_{\rm A} = 1/N)$  the sampled and periodised spectral function

$${\rm P}\{X(\mu \cdot f_{\rm A})\} = T_{\rm A} \cdot \sum_{\nu = 0 }^{N-1}

 {\rm P}\{x(\nu \cdot T_{\rm A})\}\cdot  {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi \hspace{0.05cm} \cdot \hspace{0.05cm}\nu \hspace{0.05cm}
\cdot \hspace{0.05cm}\mu /N} \hspace{0.05cm}.$$

It is taken into account that due to the discretisation, the periodised functions are to be used in each case.

For reasons of simplified notation, we now make the following substitutions:

• The  $N$  time-domain coefficients'  are with the iterating variable  $\nu = 0$, ... , $N - 1$:

$$d(\nu) =.

 {\rm P}\left\{x(t)\right\}{\big|}_{t \hspace{0.05cm}= \hspace{0.05cm}\nu \hspace{0.05cm}\cdot \hspace{0.05cm}T_{\rm A}}\hspace{0.05cm}.$$

• Let  $N$  frequency domain coefficients'  be associated with the running variable  $\mu = 0,$ ... , $N$ – 1:

$$D(\mu) = f_{\rm A} \cdot

 {\rm P}\left\{X(f)\right\}{\big|}_{f \hspace{0.05cm}= \hspace{0.05cm}\mu \hspace{0.05cm}\cdot \hspace{0.05cm}f_{\rm A}}\hspace{0.05cm}.$$

• Abbreviation is written for the from  $N$  dependent  complex rotation factor' :

$$w = {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N}

= \cos \left( {2 \pi}/{N}\right)-{\rm j} \cdot \sin \left( {2 \pi}/{N}\right)
\hspace{0.05cm}.$$ 

On Defining the Discrete Fourier Transform (DFT) with  $N=8$

$\text{Definition:}$ 

The term  Discrete Fourier Transform  (in short DFT)  means the calculation of the  $N$  spectral coefficients  $D(\mu)$  from the  $N$  signal coefficients  $d(\nu)$:

$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1}

 d(\nu)\cdot  {w}^{\hspace{0.05cm}\nu \hspace{0.07cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}. $$

In the diagram you can see

• the  $N = 8$  signal coefficients  $d(\nu)$  by the blue filling,
• the  $N = 8$  spectral coefficients  $D(\mu)$  at the green filling.

Inverse Discrete Fourier Transform

The Inverse Discrete Fourier Transform (IDFT) describes the  second Fourier integral

$$\begin{align*}x(t) & = \int_{-\infty

}^{+\infty}X(f) \cdot {\rm e}^{\hspace{0.05cm}{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi \hspace{0.05cm}\cdot \hspace{0.05cm} f \hspace{0.05cm}\cdot \hspace{0.05cm}
t}\hspace{0.1cm} {\rm d}f\end{align*}$$

in discretized form:  

$$d(\nu) =

 {\rm P}\left\{x(t)\right\}{\big|}_{t \hspace{0.05cm}= \hspace{0.05cm}\nu \hspace{0.05cm}\cdot \hspace{0.05cm}T_{\rm
 A}}\hspace{0.01cm}.$$

On the Definition of the IDFT with  $N=8$

$\text{Definition:}$ 

The term  Inverse Discrete Fourier Transform  (in short IDFT)  means the calculation of the signal coefficients  $d(\nu)$  from the spectral coefficients  $D(\mu)$:

$$d(\nu) = \sum_{\mu = 0 }^{N-1}

D(\mu) \cdot {w}^{-\nu \hspace{0.07cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$

With the run variables  $\nu = 0, \hspace{0.05cm}\text{...} \hspace{0.05cm}, N-1$  und  $\mu = 0, \hspace{0.05cm}\text{...} \hspace{0.05cm}, N-1$  gilt auch hier:

$$d(\nu) =

 {\rm P}\left\{x(t)\right\}{\big \vert}_{t \hspace{0.05cm}= \hspace{0.05cm}\nu \hspace{0.05cm}\cdot \hspace{0.05cm}T_{\rm
 A} }\hspace{0.01cm},$$

$$D(\mu) = f_{\rm A} \cdot

 {\rm P}\left\{X(f)\right\}{\big \vert}_{f \hspace{0.05cm}= \hspace{0.05cm}\mu \hspace{0.05cm}\cdot \hspace{0.05cm}f_{\rm A} }
 \hspace{0.01cm},$$

$$w = {\rm e}^{- {\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} 2 \pi /N}

\hspace{0.01cm}.$$

A comparison between the  DFT  and IDFT shows that exactly the same algorithm can be used. The only differences between IDFT and DFT are:

• The exponent of the rotation factor is to be applied with different sign.
• In the IDFT, the division by  $N$ is omitted.

Interpretation of DFT and IDFT

The graph shows the discrete coefficients in the time and frequency domain together with the periodified time-continuous functions.

Time– and Frequency range Coefficients of the DFT

When using DFT or IDFT, please note:

• According to the above definitions, the DFT coefficients  $d(ν)$  and  $D(\mu)$  always have the unit of the time function.
• Dividing  $D(\mu)$  by  $f_{\rm A}$, one obtains the spectral value  $X(\mu \cdot f_{\rm A})$.
• The spectral coefficients  $D(\mu)$  must always be complex in order to be able to consider odd time functions.
• In order to be able to transform bandpass signals in the equivalent lowpass–range, one usually also uses complex time coefficients  $d(\nu)$.
• The basic interval for  $\nu$  and  $\mu$  is usually defined as the range from  $0$  to  $N - 1$  (filled circles in the graph).
• With the complex-valued number sequences  $\langle \hspace{0.1cm}d(\nu)\hspace{0.1cm}\rangle = \langle \hspace{0.1cm}d(0), \hspace{0.05cm}\text{...} \hspace{0.05cm} , d(N-1) \hspace{0.1cm}\rangle$   and   $\langle \hspace{0.1cm}D(\mu)\hspace{0.1cm}\rangle = \langle \hspace{0.1cm}D(0), \hspace{0.05cm}\text{...} \hspace{0.05cm} , D(N-1) \hspace{0.1cm}\rangle$  DFT and IDFT are symbolised similarly to the conventional Fourier transform:

$$\langle \hspace{0.1cm} D(\mu)\hspace{0.1cm}\rangle \hspace{0.2cm}\bullet\!\!-\!\!\!-(N)\!-\!\!\!-\!\!\hspace{0.05cm}\circ\, \hspace{0.2cm} \langle \hspace{0.1cm} d(\nu) \hspace{0.1cm}\rangle \hspace{0.05cm}.$$

• If the time function  $x(t)$  is already limited to the range  $0 \le t \lt N \cdot T_{\rm A}$  then the time coefficients output by the IDFT directly give the samples of the time function:   $d(\nu) = x(\nu \cdot T_{\rm A}).$
• If  $x(t)$  is shifted with respect to the basic interval, one has to choose the association shown in  $\text{Example 3}$  between  $x(t)$  and the coefficients  $d(\nu)$ .

$\text{Example 3:}$  Die obere Grafik zeigt den unsymmetrischen Dreieckimpuls  $x(t)$, dessen absolute Breite kleiner ist als  $T_{\rm P} = N \cdot T_{\rm A}$.

On Assigning of the DFT Coefficients With  $N=8$

The sketch below shows the assigned DFT coefficients $($valid for  $N = 8)$.

• For  $\nu = 0,\hspace{0.05cm}\text{...} \hspace{0.05cm} , N/2 = 4$  $d(\nu) = x(\nu \cdot T_{\rm A})$ holds:

$$d(0) = x (0)\hspace{0.05cm}, \hspace{0.15cm}

d(1) = x (T_{\rm A})\hspace{0.05cm}, \hspace{0.15cm} d(2) = x (2T_{\rm A})\hspace{0.05cm}, $$

$$d(3) = x (3T_{\rm A})\hspace{0.05cm}, \hspace{0.15cm}

d(4) = x (4T_{\rm A})\hspace{0.05cm}.$$

• On the other hand, the coefficients  $d(5)$,  $d(6)$  and  d$(7)$  are to be set as follows:

$$d(\nu) = x \big ((\nu\hspace{-0.05cm} - \hspace{-0.05cm} N ) \cdot T_{\rm A}\big ) $$

$$ \Rightarrow \hspace{0.2cm}d(5) = x (-3T_{\rm A})\hspace{0.05cm}, \hspace{0.35cm}

d(6) = x (-2T_{\rm A})\hspace{0.05cm}, \hspace{0.35cm} d(7) = x (-T_{\rm A})\hspace{0.05cm}.$$

Exercises for The Chapter

Exercise 5.2: Inverse Discrete Fourier Transform

Exercise 5.2Z: DFT of a Triangular Pulse