Aufgaben:Exercise 3.1: Causality Considerations: Difference between revisions

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[[File:EN_LZI_A_3_1.png|right|frame|Two two-port networks]]
[[File:EN_LZI_A_3_1.png|right|frame|Two two-port networks]]
The graph shows above the two-port network with the transfer function
The graph shows above the two-port network with the transfer function
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm},$$
\hspace{0.05cm},$$


where  $f_{\rm G}$  represents the  $\rm 3dB$  cut-off frequency:
where  $f_{\rm G}$  represents the  $\rm 3dB$  cut-off frequency:
:$$f_{\rm G} = \frac{R}{2 \pi \cdot L}
:$$f_{\rm G} = \frac{R}{2 \pi \cdot L}\hspace{0.05cm}.$$
\hspace{0.05cm}.$$


By cascading  $n$  two-port networks  $H_1(f)$  built in the same way, the following transfer function is obtained:
By cascading  $n$  two-port networks  $H_1(f)$  built in the same way, the following transfer function is obtained:
:$$H_n(f) = \big [H_1(f)\big ]^n =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^n}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^n}
:$$H_n(f) = \big [H_1(f)\big ]^n =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^n}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^n}\hspace{0.05cm}.$$
\hspace{0.05cm}.$$  


*Here, a suitable resistor decoupling is presumed,  but this is not important for solving this exercise.  
*Here, a suitable resistor decoupling is presumed,  but this is not important for solving this exercise.  
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For any causal system,  the real and imaginary parts of the spectral function  $H(f)$  satisfy the  [[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem#Hilbert transformation|Hilbert transformation]],  which is expressed by the following abbreviation:
For any causal system,  the real and imaginary parts of the spectral function  $H(f)$  satisfy the  [[Linear_and_Time_Invariant_Systems/Conclusions_from_the_Allocation_Theorem#Hilbert transformation|Hilbert transformation]],  which is expressed by the following abbreviation:
:$${\rm Im} \left\{ H(f) \right \}  \quad
:$${\rm Im} \left\{ H(f) \right \}  \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad{\rm Re} \left\{ H(f) \right \}\hspace{0.05cm}.$$
\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad
{\rm Re} \left\{ H(f) \right \}\hspace{0.05cm}.$$


Since the Hilbert transformation provides important information not only for transfer functions but also for time signals,  the correspondence is often expressed by the general variable  $x$,  which is to be interpreted - depending on the application - as normalized frequency or as normalized time.
Since the Hilbert transformation provides important information not only for transfer functions but also for time signals,  the correspondence is often expressed by the general variable  $x$,  which is to be interpreted - depending on the application - as normalized frequency or as normalized time.
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*Every real network is causal.  The impulse response  $h(t)$  is equal to the output signal  $y(t)$  if at time  $t= 0$  an extremely short impulse – a so-called Dirac delta impulse – is applied to the input.  
*Every real network is causal.  The impulse response  $h(t)$  is equal to the output signal  $y(t)$  if at time  $t= 0$  an extremely short impulse – a so-called Dirac delta impulse – is applied to the input.  
*Then,&nbsp; a signal cannot occur at the output already for times&nbsp; $t< 0$&nbsp; for causality reasons:
*Then,&nbsp; a signal cannot occur at the output already for times&nbsp; $t< 0$&nbsp; for causality reasons:
:$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}
:$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}t<0 \hspace{0.05cm}.$$
t<0 \hspace{0.05cm}.$$
*Formally, this can be shown as follows: &nbsp; The high-pass transfer function&nbsp; $H_1(f)$&nbsp; can be rearranged as follows:
*Formally, this can be shown as follows: &nbsp; The high-pass transfer function&nbsp; $H_1(f)$&nbsp; can be rearranged as follows:
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}
:$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}= 1- \frac{1}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm}.$$
= 1- \frac{1}{1+{\rm j}\cdot f/f_{\rm G}}
\hspace{0.05cm}.$$
*The second transfer function describes the low-pass function equivalent to&nbsp; $H_1(f)$,&nbsp; which results in the exponential function in the time domain.  
*The second transfer function describes the low-pass function equivalent to&nbsp; $H_1(f)$,&nbsp; which results in the exponential function in the time domain.  
*The&nbsp; "$1$"&nbsp; becomes a Dirac delta function.&nbsp; Considering&nbsp; $T = 2\pi \cdot f_{\rm G}$&nbsp; the following thus holds for&nbsp; $t \ge 0$:
*The&nbsp; "$1$"&nbsp; becomes a Dirac delta function.&nbsp; Considering&nbsp; $T = 2\pi \cdot f_{\rm G}$&nbsp; the following thus holds for&nbsp; $t \ge 0$:
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'''(3)'''&nbsp; The series connection of two high-pass filters results in the following transfer function:
'''(3)'''&nbsp; The series connection of two high-pass filters results in the following transfer function:
:$$H_2(f) = \big [H_1(f)\big ]^2  =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^2}
:$$H_2(f) = \big [H_1(f)\big ]^2  =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^2}=\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2 \cdot \big [(1-{\rm j}\cdot f/f_{\rm G})\big ]^2}{\big [(1+{\rm j}\cdot f/f_{\rm G}) \cdot (1-{\rm j}\cdot f/f_{\rm G})\big ]^2}=  \frac{(f/f_{\rm G})^4 - (f/f_{\rm G})^2 +{\rm j}\cdot 2\cdot  (f/f_{\rm G})^3)}{\big [1+(f/f_{\rm G})^2 \big ]^2}\hspace{0.05cm}.$$
=\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2 \cdot \big [(1-{\rm j}\cdot f/f_{\rm G})\big ]^2}
{\big [(1+{\rm j}\cdot f/f_{\rm G}) \cdot (1-{\rm j}\cdot f/f_{\rm G})\big ]^2}=  \frac{(f/f_{\rm G})^4 - (f/f_{\rm G})^2 +{\rm j}\cdot 2
\cdot  (f/f_{\rm G})^3)}
{\big [1+(f/f_{\rm G})^2 \big ]^2}\hspace{0.05cm}.$$


*With&nbsp; $f = f_{\rm G}$&nbsp; from this it follows that:
*With&nbsp; $f = f_{\rm G}$&nbsp; from this it follows that:
:$$H_2(f = f_{\rm G})  = \frac{1 - 1 +{\rm j}\cdot 2}
:$$H_2(f = f_{\rm G})  = \frac{1 - 1 +{\rm j}\cdot 2}{4}= {\rm j} /{2} \hspace{0.5cm}\Rightarrow \hspace{0.5cm}{\rm Re} \left\{ H_2(f = f_{\rm G}) \right \}  \hspace{0.15cm}\underline{  = 0}, \hspace{0.4cm}{\rm Im} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0.5}\hspace{0.05cm}.$$
{4}= {\rm j} /{2} \hspace{0.5cm}\Rightarrow \hspace{0.5cm}{\rm Re} \left\{ H_2(f = f_{\rm G}) \right \}  \hspace{0.15cm}\underline{  = 0}, \hspace{0.4cm}
{\rm Im} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0.5}\hspace{0.05cm}.$$




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*However,&nbsp; the real and imaginary parts of the spectral function &nbsp;$H_2(f)$&nbsp; are related via the Hilbert transformation for a causal impulse response &nbsp;$h_2(t)$&nbsp;.&nbsp;  
*However,&nbsp; the real and imaginary parts of the spectral function &nbsp;$H_2(f)$&nbsp; are related via the Hilbert transformation for a causal impulse response &nbsp;$h_2(t)$&nbsp;.&nbsp;  
*Thus,&nbsp; considering the shortcut &nbsp;$x = f/f_{\rm G}$&nbsp; and the result of the subtask&nbsp; '''(3)'''&nbsp; the following holds:
*Thus,&nbsp; considering the shortcut &nbsp;$x = f/f_{\rm G}$&nbsp; and the result of the subtask&nbsp; '''(3)'''&nbsp; the following holds:
:$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad
:$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$
\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad
\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$
{{ML-Fuß}}
{{ML-Fuß}}



Revision as of 15:48, 16 March 2026

Two two-port networks

The graph shows above the two-port network with the transfer function

$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm},$$

where  $f_{\rm G}$  represents the  $\rm 3dB$  cut-off frequency:

$$f_{\rm G} = \frac{R}{2 \pi \cdot L}\hspace{0.05cm}.$$

By cascading  $n$  two-port networks  $H_1(f)$  built in the same way, the following transfer function is obtained:

$$H_n(f) = \big [H_1(f)\big ]^n =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^n}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^n}\hspace{0.05cm}.$$
  • Here, a suitable resistor decoupling is presumed,  but this is not important for solving this exercise.
  • The lower graph shows for example the realization of the transfer function  $H_2(f)$.


In this exercise,  such a two-port network is considered with respect to its causality properties.

For any causal system,  the real and imaginary parts of the spectral function  $H(f)$  satisfy the  Hilbert transformation,  which is expressed by the following abbreviation:

$${\rm Im} \left\{ H(f) \right \} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad{\rm Re} \left\{ H(f) \right \}\hspace{0.05cm}.$$

Since the Hilbert transformation provides important information not only for transfer functions but also for time signals,  the correspondence is often expressed by the general variable  $x$,  which is to be interpreted - depending on the application - as normalized frequency or as normalized time.



Please note:


Questions

1 How can  $H_1(f)$  be characterized?

$H_1(f)$  describes a low-pass filter.
$H_1(f)$  describes a high-pass filter.

2 Does  $H_1(f)$  describe a causal network?

Yes.
No.

3 Compute the transfer function  $H_2(f)$.   What is the complex value for  $f = f_{\rm G}$?

${\rm Re}\big[H_2(f = f_{\rm G})\big] \ = \ $
${\rm Im}\big[H_2(f = f_{\rm G})\big] \ = \ $

4 Which of the following statements are true?

$H_2(f)$  describes a causal system.
The expressions  $(x^4 - x^2)/(x^4 +2 x^2 + 1)$  and  $2x^3/(x^4 +2 x^2 + 1)$  are a Hilbert pair.
The causality condition is not satisfied for  $n > 2$ .


Solution

(1)  Proposed solution 2  is correct:

  • The given transfer function can be computed according to the voltage divider principle.   The following holds:
$$H_1(f = 0) = 0, \hspace{0.2cm}H_1(f \rightarrow \infty) = 1$$
  • This is a high-pass filter.
  • For very low frequencies, the inductivity  $L$  constitutes a short circuit.


(2)  Yes  is correct:

  • Every real network is causal.  The impulse response  $h(t)$  is equal to the output signal  $y(t)$  if at time  $t= 0$  an extremely short impulse – a so-called Dirac delta impulse – is applied to the input.
  • Then,  a signal cannot occur at the output already for times  $t< 0$  for causality reasons:
$$y(t) = h(t) = 0 \hspace{0.2cm}{\rm{f\ddot{u}r}} \hspace{0.2cm}t<0 \hspace{0.05cm}.$$
  • Formally, this can be shown as follows:   The high-pass transfer function  $H_1(f)$  can be rearranged as follows:
$$H_1(f) = \frac{{\rm j}\cdot f/f_{\rm G}}{1+{\rm j}\cdot f/f_{\rm G}}= 1- \frac{1}{1+{\rm j}\cdot f/f_{\rm G}}\hspace{0.05cm}.$$
  • The second transfer function describes the low-pass function equivalent to  $H_1(f)$,  which results in the exponential function in the time domain.
  • The  "$1$"  becomes a Dirac delta function.  Considering  $T = 2\pi \cdot f_{\rm G}$  the following thus holds for  $t \ge 0$:
$$h_1(t) = \delta(t) - {1}/{T} \cdot {\rm e}^{-t/T} \hspace{0.05cm}.$$
  • In contrast,  $h_1(t)= 0$ holds for  $t< 0$,  which would prove causality.


(3)  The series connection of two high-pass filters results in the following transfer function:

$$H_2(f) = \big [H_1(f)\big ]^2 =\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2}{\big [1+{\rm j}\cdot f/f_{\rm G}\big ]^2}=\frac{\big [{\rm j}\cdot f/f_{\rm G}\big ]^2 \cdot \big [(1-{\rm j}\cdot f/f_{\rm G})\big ]^2}{\big [(1+{\rm j}\cdot f/f_{\rm G}) \cdot (1-{\rm j}\cdot f/f_{\rm G})\big ]^2}= \frac{(f/f_{\rm G})^4 - (f/f_{\rm G})^2 +{\rm j}\cdot 2\cdot (f/f_{\rm G})^3)}{\big [1+(f/f_{\rm G})^2 \big ]^2}\hspace{0.05cm}.$$
  • With  $f = f_{\rm G}$  from this it follows that:
$$H_2(f = f_{\rm G}) = \frac{1 - 1 +{\rm j}\cdot 2}{4}= {\rm j} /{2} \hspace{0.5cm}\Rightarrow \hspace{0.5cm}{\rm Re} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0}, \hspace{0.4cm}{\rm Im} \left\{ H_2(f = f_{\rm G}) \right \} \hspace{0.15cm}\underline{ = 0.5}\hspace{0.05cm}.$$


(4)  The first two proposed solutions  are correct:

  • Since the impulse response is  $h_1(t) = 0$  for  $t < 0$,  the convolution operation  $h_2(t) = h_1(t) \star h_1(t)$  also satisfies the causality condition. 
  • Similarly, the  $n$–fold convolution yields a causal impulse response:   $h_n(t) = 0 \hspace{0.2cm}{\rm{for}} \hspace{0.2cm}
t<0 \hspace{0.05cm}.$
  • However,  the real and imaginary parts of the spectral function  $H_2(f)$  are related via the Hilbert transformation for a causal impulse response  $h_2(t)$ . 
  • Thus,  considering the shortcut  $x = f/f_{\rm G}$  and the result of the subtask  (3)  the following holds:
$$\frac{x^4- x^2}{x^4+2 x^2+1} \quad\bullet\!\!-\!\!\!-\!\!\!-\!\!\hspace{-0.05cm}\rightarrow\quad\frac{2x^3}{x^4+2 x^2+1}\hspace{0.05cm}.$$