Difference between revisions of "Aufgaben:Exercise 4.13: Gaussian ACF and PSD"

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}}
 
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[[File:P_ID411__Sto_A_4_13.png|right|frame|Two Gaussian ACF]]
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[[File:P_ID411__Sto_A_4_13.png|right|frame|Two Gaussian ACFs]]
Let the random process considered here  $\{x_i(t)\}$  be characterized by the autocorrelation function (ACF) outlined above  This random process is mean-free and the equivalent ACF duration is  ${ {\rm \nabla}  }\tau_x = 5 \hspace{0.08cm} \rm µ s$:
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Let the random process considered here  $\{x_i(t)\}$  be characterized by the auto-correlation function  $\rm (ACF)$  outlined above  This random process is mean-free and the equivalent ACF duration is  ${ {\rm \nabla}  }\tau_x = 5 \hspace{0.08cm} \rm µ s$:
 
:$$\varphi_x(\tau)=\rm 0.25 V^2\cdot \rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\tau}{/ 5 \hspace{0.08cm}{\rm µ}s })^2} .$$
 
:$$\varphi_x(\tau)=\rm 0.25 V^2\cdot \rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\tau}{/ 5 \hspace{0.08cm}{\rm µ}s })^2} .$$
The bottom figure shows the ACF of the process  $\{y_i(t)\}$  This reads with the equivalent ACF duration  ${ {\rm \nabla}  }\tau_y = 10 \hspace{0.08cm} \rm µ s$:
+
The bottom graph shows the ACF of the process  $\{y_i(t)\}$.  This reads with the equivalent ACF duration  ${ {\rm \nabla}  }\tau_y = 10 \hspace{0.08cm} \rm µ s$:
 
:$$ \varphi_y(\tau)=\rm 0.16 V^2 + \rm 0.09 V^2\cdot\rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\tau}/{\nabla \it \tau_y})^2} .$$
 
:$$ \varphi_y(\tau)=\rm 0.16 V^2 + \rm 0.09 V^2\cdot\rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\tau}/{\nabla \it \tau_y})^2} .$$
  
In this exercise, the Power spectral densities of the two processes are sought.
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In this exercise,  the power-spectral densities of the two processes are sought.
 
 
 
 
 
 
 
 
 
 
  
  
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Hints:
 
Hints:
*This exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Power-Spectral_Density|Power-Spectral Density]].
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*This exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Power-Spectral_Density|Power-spectral density]]  $\rm (PSD)$.
*Reference is also made to the chapter  [[Theory_of_Stochastic_Signals/Auto-Correlation_Function|Auto-correlation function]].
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*Reference is also made to the chapter  [[Theory_of_Stochastic_Signals/Auto-Correlation_Function|Auto-correlation function]]  $\rm (ACF)$.  
 
 
*To solve this exercise you can use the following Fourier correspondence:
 
*To solve this exercise you can use the following Fourier correspondence:
:$$\rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\it f}/{\rm \Delta\it f})^2}\ \bullet\!\!-\!\!-\!\!-\!-\!\!\circ\, \ {\rm \Delta \it f} \cdot \rm e^{-\pi \hspace{0.05cm}\cdot \hspace{0.05cm} ({\rm \Delta\it f} \hspace{0.05cm}\cdot \hspace{0.05cm}\it t {\rm )}^{\rm 2}}.$$
+
:$$\rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\it f}/{\rm \Delta\it f})^2}\ \bullet\!\!-\!\!\!-\!\!\!-\!\!\circ\,\ {\rm \Delta \it f} \cdot \rm e^{-\pi \hspace{0.05cm}\cdot \hspace{0.05cm} ({\rm \Delta\it f} \hspace{0.05cm}\cdot \hspace{0.05cm}\it t {\rm )}^{\rm 2}}.$$
  
  
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{What is  ${\it \Phi}_x(f)$?  Give the PSD values for  $f= 0$  and  $f = 200 \hspace{0.08cm} \rm kHz$  on.
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{What is the power-spectral density  ${\it \Phi}_x(f)$?  Give the PSD values for  $f= 0$  and  $f = 200 \hspace{0.08cm} \rm kHz$.
 
|type="{}"}
 
|type="{}"}
 
${\it \Phi}_x(f = 0)\ = \ $ { 1.25 3% } $\ \cdot 10^{-6} \ \rm V^2\hspace{-0.1cm}/Hz$
 
${\it \Phi}_x(f = 0)\ = \ $ { 1.25 3% } $\ \cdot 10^{-6} \ \rm V^2\hspace{-0.1cm}/Hz$
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{Which statements are valid, if the random process has no periodic parts?  Furthermore, a constant power is assumed.
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{Which statements are valid,  if the random process has no periodic parts?  Furthermore,  a constant power is assumed.
 
|type="[]"}
 
|type="[]"}
 
+ The process power is the integral over the PSD.
 
+ The process power is the integral over the PSD.
+ If the process is zero mean, the PSD is always continuous.
+
+ If the process is zero mean,  the PSD is always continuous.
- The wider the ACF, the wider the PSD.
+
- The wider the ACF,  the wider the PSD.
 
+ A wider ACF results in higher PSD values.
 
+ A wider ACF results in higher PSD values.
{Calculate the Power spectral density spectrum  ${\it \Phi}_y(f)$.  What are the values for the continuous PSD component at  $f= 0$  and  $f = 200 \hspace{0.08cm}  \rm kHz$?
+
 
 +
{Calculate the power-spectral density  ${\it \Phi}_y(f)$.  What are the values for the continuous PSD component at  $f= 0$  and  $f = 200 \hspace{0.08cm}  \rm kHz$?
 
|type="{}"}
 
|type="{}"}
 
${\it \Phi}_y(f = 0)\ = \ $ { 0.9 3% } $\ \cdot 10^{-6} \ \rm V^2\hspace{-0.1cm}/Hz$
 
${\it \Phi}_y(f = 0)\ = \ $ { 0.9 3% } $\ \cdot 10^{-6} \ \rm V^2\hspace{-0.1cm}/Hz$
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{Which of the following statements are true regarding the process  $\{y_i(t)\}$?
 
{Which of the following statements are true regarding the process  $\{y_i(t)\}$?
 
|type="[]"}
 
|type="[]"}
- The PSD involves a Dirac at frequency $ f = {\rm \nabla} \hspace{-0.05cm} f_y$.
+
- The PSD involves a Dirac delta function at frequency $ f = {\rm \nabla} \hspace{-0.05cm} f_y$.
+ The PSD involves a Dirac at frequency $f= 0$.
+
+ The PSD involves a Dirac delta function at frequency $f= 0$.
- Dirac weight and continuous PSD have the same unit.
+
- The Dirac weight and the continuous PSD have the same unit.
  
  

Revision as of 15:43, 25 March 2022

Two Gaussian ACFs

Let the random process considered here  $\{x_i(t)\}$  be characterized by the auto-correlation function  $\rm (ACF)$  outlined above  This random process is mean-free and the equivalent ACF duration is  ${ {\rm \nabla} }\tau_x = 5 \hspace{0.08cm} \rm µ s$:

$$\varphi_x(\tau)=\rm 0.25 V^2\cdot \rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\tau}{/ 5 \hspace{0.08cm}{\rm µ}s })^2} .$$

The bottom graph shows the ACF of the process  $\{y_i(t)\}$.  This reads with the equivalent ACF duration  ${ {\rm \nabla} }\tau_y = 10 \hspace{0.08cm} \rm µ s$:

$$ \varphi_y(\tau)=\rm 0.16 V^2 + \rm 0.09 V^2\cdot\rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\tau}/{\nabla \it \tau_y})^2} .$$

In this exercise,  the power-spectral densities of the two processes are sought.



Hints:

$$\rm e^{-\pi \hspace{0.03cm}\cdot \hspace{0.03cm} ({\it f}/{\rm \Delta\it f})^2}\ \bullet\!\!-\!\!\!-\!\!\!-\!\!\circ\,\ {\rm \Delta \it f} \cdot \rm e^{-\pi \hspace{0.05cm}\cdot \hspace{0.05cm} ({\rm \Delta\it f} \hspace{0.05cm}\cdot \hspace{0.05cm}\it t {\rm )}^{\rm 2}}.$$


Questions

1

What is the equivalent PSD bandwidth of the process  $\{x_i(t)\}$?

$ {\rm \nabla} \hspace{-0.05cm} f_x \ = \ $

$\ \rm kHz$

2

What is the power-spectral density  ${\it \Phi}_x(f)$?  Give the PSD values for  $f= 0$  and  $f = 200 \hspace{0.08cm} \rm kHz$.

${\it \Phi}_x(f = 0)\ = \ $

$\ \cdot 10^{-6} \ \rm V^2\hspace{-0.1cm}/Hz$
${\it \Phi}_x(f = 200 \hspace{0.08cm} \rm kHz)\ = \ $

$\ \cdot 10^{-6} \ \rm V^2\hspace{-0.1cm}/Hz$

3

Which statements are valid,  if the random process has no periodic parts?  Furthermore,  a constant power is assumed.

The process power is the integral over the PSD.
If the process is zero mean,  the PSD is always continuous.
The wider the ACF,  the wider the PSD.
A wider ACF results in higher PSD values.

4

Calculate the power-spectral density  ${\it \Phi}_y(f)$.  What are the values for the continuous PSD component at  $f= 0$  and  $f = 200 \hspace{0.08cm} \rm kHz$?

${\it \Phi}_y(f = 0)\ = \ $

$\ \cdot 10^{-6} \ \rm V^2\hspace{-0.1cm}/Hz$
${\it \Phi}_y(f = 200 \hspace{0.08cm} \rm kHz)\ = \ $

$\ \cdot 10^{-6} \hspace{0.05cm} \rm V^2\hspace{-0.1cm}/Hz$

5

Which of the following statements are true regarding the process  $\{y_i(t)\}$?

The PSD involves a Dirac delta function at frequency $ f = {\rm \nabla} \hspace{-0.05cm} f_y$.
The PSD involves a Dirac delta function at frequency $f= 0$.
The Dirac weight and the continuous PSD have the same unit.


Solution

(1)  The equivalent PSD bandwidth is the reciprocal of the equivalent ACF duration:

$$\nabla f_x = 1 / \nabla \tau_x \hspace{0.15cm}\underline{= {\rm 200\hspace{0.1cm}kHz}}.$$


(2)  One can adapt the given Fourier correspondence to the task as follows:

$$K\cdot{\rm e}^{-\pi({\tau}/{\nabla\tau_x})^2}\ \circ\!\!-\!\!-\!\!-\!\!-\!-\!\bullet\,\ \frac{\it K}{\nabla \it f_x}\cdot{\rm e}^{-\pi({f}/{\nabla f_x})^2}.$$
  • With  $K = 0.25 \hspace{0.05cm}\rm V^2$  and  $ {\rm \nabla} \hspace{-0.05cm} f_x = 200\hspace{0.05cm} \rm kHz$  obtains:
$${\it \Phi_x}(f)=1.25\cdot\rm 10^{-\rm 6}\hspace{0.1cm}\frac{V^2}{Hz}\cdot\rm e^{-\pi({\it f}/{\nabla\it f_x})^2}\hspace{0.3cm} \Rightarrow \hspace{0.3cm}{\it \Phi_x}(f = 0)=\hspace{0.15cm}\underline{\rm 1.25 \cdot 10^{-6} \hspace{0.1cm} V^2\hspace{-0.1cm}/Hz}, \hspace{0.5cm}{\it \Phi_x}(f = 200 \hspace{0.05cm} \rm kHz)=\hspace{0.15cm}\underline{\rm 0.054 \cdot 10^{-6} \hspace{0.1cm} V^2\hspace{-0.1cm}/Hz}.$$


(3)  Correct solutions 1, 2, and 4:

  • A mean-free process always results in a continuous PSD.  This is narrower the wider the ACF is (reciprocitylaw).
  • The process power is equal to the integral of the PSD.
  • Therefore, at constant power, a wider ACF  (narrower PSD)  must be compensated by higher PSD values.
  • A DC component or periodic components always result in dirac functions in the PSD;  otherwise, the PSD is always continuous in value.


(4)  Analogous to subtask  (2)  holds with  $ {\rm \nabla} \hspace{-0.05cm} f_y = 100\hspace{0.05cm} \rm kHz$:

$${\it \Phi_y}(f)=\frac{\rm 0.09 V^2}{\nabla\it f_y}\cdot\rm e^{-\pi({\it f}/{\nabla\it f_y})^2}+\it m_y^{\rm 2}\cdot\delta(f).$$
  • Because of the DC component, there is a Dirac at frequency $f = 0$ in addition to the continuous PSD component.
  • The continuous PSD–part at $f= 0$ is  ${\it \Phi_y}(f = 0)=\hspace{0.15cm}\underline{\rm 0.9 \cdot 10^{-6} \hspace{0.1cm} V^2\hspace{-0.1cm}/Hz}.$
  • The fraction at $f = 2 \cdot {\rm \nabla} \hspace{-0.05cm} f_y = 200 \hspace{0.05cm}\rm kHz$  is increased by a factor  ${\rm e}^{-4} \approx 0.0183$  lower   ⇒   ${\it \Phi_y}(f )=\hspace{0.15cm}\underline{\rm 0.0165 \cdot 10^{-6} \hspace{0.1cm} V^2\hspace{-0.1cm}/Hz}.$


(5)  Correct is only the second proposed solution:

  • The PSD of a mean-valued process generally involves a Dirac function at $f=0$  with weight  $m_y^2$.
  • In the present case, this value is equal to  $0.16 \ \rm V^2$.
  • Since  $\delta(f)$  has unit  $\rm 1/Hz = s$ , the units of the continuous and discrete PSD components differ.