Direct Current Signal - Limit Case of a Periodic Signal

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Time signal representation

$\text{Definition:}$  A  $\text{direct current (DC) signal}$   is a deterministic signal whose instantaneous values are constant for all times  $t$  from  $-\infty$  to  $+\infty$.  Such a signal is the borderline case of a  harmonic oscillation, where the period duration  $T_{0}$  has an infinitely large value.

DC signal in time domain

According to this definition a DC signal always ranges from  $t = -\infty$  to  $t = +\infty$.  If the constant signal is only switched on at the time  $t = 0$  there is no DC signal.

  • A direct signal can never be a carrier of information in a communication system, but message signals can possess a  "direct signal part". 
  • All statements made in the following for the direct current signal apply in the same way also to such a direct signal component.

$\text{Definition:}$  For the  $\text{DC signal component}$  $A_{0}$  of any signal  $x(t)$  applies:

$$A_0 = \lim_{T_{\rm M}\to\infty}\,\frac{1}{T_{\rm M} }\cdot\int^{T_{\rm M}/2}_{-T_{\rm M}/2}x(t)\,{\rm d} t. $$
  • The measurement duration  $T_{\rm M}$  should always be selected as large as possible (infinite in borderline cases).
  • The given equation is only valid if  $T_{\rm M}$  lies symmetrical about the time  $t=0$.

Random signal with DC componentl

$\text{Example 1:}$  The graph shows a random signal  $x(t)$.

  • The DC component  $A_{0}$  is here  $2\ \rm V$.
  • In the sense of statistics,  $A_{0}$  corresponds to the linear mean.

Spectral representation

We now look at the situation in the frequency domain.  From the time function it is already obvious, that it contains - spectrally speaking - only one single (physical) frequency, namely the frequency  $f=0$.

This result shall now be derived mathematically.  In anticipation of the chapter  Fourier Transform  the connection between the time signal  $x(t)$  and the corresponding spectrum  $X(f)$  is already given here:

$$X(f)= \hspace{0.05cm}\int_{-\infty} ^{{+}\infty} x(t) \, \cdot \, { \rm e}^{-\rm j 2\pi \it ft} \,{\rm d}t.$$

The spectral function  $X(f)$  is called after the French mathematician  Jean Baptiste Joseph Fourier  the Fourier transform of  $x(t)$  and the short name for this functional relation is

$$X(f)\ \bullet\!\!-\!\!\!-\!\!\circ\,\ x(t).$$

For example, if  $x(t)$  describes a voltage curve, so  $X(f)$  has the unit  "V/Hz".

Applying the Fourier transform to the DC signal  $x(t)=A_{0}$  yields the spectral function

$$X(f)= A_0 \cdot \int_{-\infty} ^{+\hspace{0.01cm}\infty}\rm e \it ^{-\rm {j 2\pi} \it ft} \,{\rm d}t$$

with the following properties:

  • The integral diverges for  $f=0$, i.e. it returns an infinitely large value  $($integration over the constant value  $1)$.
  • For a frequency  $f\ne 0$  on the other hand, the integral is zero;  the corresponding proof, however, is not trivial (see next page).

$\text{Definition:}$  The searched spectral function  $X(f)$  is compactly expressed by the following equation

$$X(f) = A_0 \, \cdot \, \rm \delta(\it f).$$
  •  $\delta(f)$  is denoted as the  $\text{Dirac function}$,  also known as  " distribution".
  •  $\delta(f)$  is a mathematically complicated function; the derivation can be found on the next page.

DC signal and its spectral function

$\text{Example 2:}$  The graphic shows the functional connection

  • between an DC signal  $x(t)=A_{0}$  and
  • its corresponding spectral function  $X(f)=A_{0} \cdot \delta(f)$.

The Dirac function at frequency  $f=0$  is represented by an arrow with the weight  $A_{0}$.

Dirac (delta) function in frequency domain

$\text{Definition:}$  The  $\text{Dirac delta function}$   ⇒   short:  $\text{Dirac function}$  has the following properties:

  • The Dirac function is infinitely narrow, i.e. it is  $\delta(f)=0$  for  $f \neq 0$.
  • The Dirac function  $\delta(f)$  is infinitely high at the frequency  $f = 0$ .
  • The Dirac weight  $($area of the Dirac function$)$  yields a finite value, namely  $1$:
$$\int_\limits{-\infty} ^{+\infty} \delta( f)\,{\rm d}f =1.$$
  • It follows from this last property that  $\delta(f)$  has the unit  ${\rm Hz}^{-1} = {\rm s}$ .

The derivation of the Dirac function

$\text{Proof:}$  For the mathematical derivation of these properties we assume a dimensionless direct signal  $x(t)$.

  • To force the convergence of the Fourier integral, the non-energy-limited signal  $x(t)$  is multiplied by a bilateral declining exponential function. The graph shows the signal  $x(t)=1$  and the energy-limited signal
$$x_{\varepsilon} (t) = \rm e^{\it -\varepsilon \hspace{0.05cm} \cdot \hspace{0.05cm} \vert \hspace{0.01cm} t \hspace{0.01cm}\vert}{.}$$
It applies  $\varepsilon > 0$.  At the limit   $\varepsilon \to 0$ ,   $x_{\varepsilon}(t)$  passes to  $x(t)=1$.
  • The spectral representation is obtained by applying the Fourier integral given above:
$$X_\varepsilon (f)=\int_{-\infty}^{0} {\rm e}^{\varepsilon{t} }\, {\cdot}\, {\rm e}^{-\rm j 2\pi \it ft} \,{\rm d}t \hspace{0.2cm}+ \hspace{0.2cm} \int_{0}^{+\infty} {\rm e}^{-\varepsilon t} \,{\cdot}\, { \rm e}^{-\rm j 2\pi \it ft} \,{\rm d}t.$$
  • After integration and combination of both parts we obtain the purely real spectral function of the energy-limited signal  $x_{\varepsilon}(t)$:
$$X_\varepsilon (f)=\frac{1}{\varepsilon -\rm j \cdot 2\pi \it f} + \frac{1}{\varepsilon+\rm j \cdot 2\pi \it f} = \frac{2\varepsilon}{\varepsilon^2 + (\rm 2\pi {\it f}\hspace{0.05cm} ) \rm ^2} \, .$$
  • The area under the  $X_\varepsilon (f)$ curve is independent of the parameter  $\varepsilon$  equals  $1$. The smaller  $ε$  is selected, the narrower and higher the function becomes, as the following (German language) learning video shows:
           Herleitung und Visualisierung der Diracfunktion   ⇒   "Derivation and visualization of the Dirac function".
  • The limit for  $\varepsilon \to 0$  returns the Dirac function with the weight  $1$:
$$\lim_{\varepsilon \hspace{0.05cm} \to \hspace{0.05cm} 0}X_\varepsilon (f)= \delta(f).$$

Exercises for the chapter

Exercise 2.2: Direct Current Component of Signals

Exercise 2.2Z: Non–Linearities