Difference between revisions of "Aufgaben:Exercise 2.7: Coherence Bandwidth"

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$\sigma_{\rm V} \ = \ ${ 1 3% } $\ \rm µ s$
 
$\sigma_{\rm V} \ = \ ${ 1 3% } $\ \rm µ s$
  
{What equation applies to the frequency–correlation function  $\varphi_{\rm F}(\Delta f)$?
+
{What is the frequency–correlation function  $\varphi_{\rm F}(\Delta f)$?
 
|type="()"}
 
|type="()"}
 
+ $\varphi_{\rm F}(\Delta f) = \big[1/\tau_0 + {\rm j} \ 2 \pi \cdot \delta f \big]^{-1}$,
 
+ $\varphi_{\rm F}(\Delta f) = \big[1/\tau_0 + {\rm j} \ 2 \pi \cdot \delta f \big]^{-1}$,
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===Sample solution===
 
===Sample solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''  The integral over the power spectral density of the delay delivers with ${\it \Phi}_0 = {\it \Phi}_{\rm V}(\tau = 0)$ the result
+
'''(1)'''  Let ${\it \Phi}_0 = {\it \Phi}_{\rm V}(\tau = 0)$. The integral of the power spectral density of the delay gives
$$\int_{0}^{+\infty} {\it \Phi}_{\rm V}(\tau) \hspace{0.15cm}{\rm d} \tau =   
+
:$$\int_{0}^{+\infty} {\it \Phi}_{\rm V}(\tau) \hspace{0.15cm}{\rm d} \tau =   
 
  {\it \Phi}_{\rm 0} \cdot \int_{0}^{+\infty} {\rm e}^{-\tau / \tau_0} \hspace{0.15cm}{\rm d} \tau =  
 
  {\it \Phi}_{\rm 0} \cdot \int_{0}^{+\infty} {\rm e}^{-\tau / \tau_0} \hspace{0.15cm}{\rm d} \tau =  
 
  {\it \Phi}_{\rm 0} \cdot \tau_0
 
  {\it \Phi}_{\rm 0} \cdot \tau_0
 
  \hspace{0.05cm}. $$
 
  \hspace{0.05cm}. $$
  
*This gives for the probability density function
+
*The probability density function is then
$$f_{\rm V}(\tau) = \frac{{{\it \phi}_{\rm V}(\tau) }{{\it \phi}_{\rm 0} \cdot \tau_0}= \frac{1}{\frac{1}{\a_dot_0} \cdot {\rm e}^{-\tau / \tau_0}  
+
:$$f_{\rm V}(\tau) = \frac{{\it \Phi}_{\rm V}(\tau) }{{\it \Phi}_{\rm 0} \cdot \tau_0}= \frac{1}{\tau_0} \cdot {\rm e}^{-\tau / \tau_0}  
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
 +
  
 
*<u>Solution 2</u> is therefore correct.
 
*<u>Solution 2</u> is therefore correct.
Line 75: Line 76:
  
  
'''(2)'''&nbsp; The $k$&ndash;te moment of a [[Stochastic_SignalingTheory/Exponential_Random_Gr%C3%B6%C3%9Fen#One-Sided_Exponential_Distribution|Exponential_Random_Size]] is equal to $m_k = k according to our nomenclature! \cdot \tau_0^k$.  
+
'''(2)'''&nbsp; The $k$-th moment of an [[Stochastische_Signaltheorie/Exponentialverteilte_Zufallsgr%C3%B6%C3%9Fen#Einseitige_Exponentialverteilung| exponential random variable]] is $m_k = k! \cdot \tau_0^k$.  
*With $k = 1$ this results in the linear mean value $m_1 = m_{\rm V}$:
+
*With $k = 1$, this results in the linear mean value $m_1 = m_{\rm V}$:
$$m_{\rm V} = \tau_0 \hspace{0.1cm} \underline {= 1\,{\rm & micro; s}
+
:$$m_{\rm V} = \tau_0 \hspace{0.1cm} \underline {= 1\,{\rm &micro; s}}
 
   \hspace{0.05cm}. $$
 
   \hspace{0.05cm}. $$
  
'''(3)'''&nbsp; According to the [[Stochastic_Signal Theory/Expected_Values_and_Moments#Some_h.C3.A4Used_Central_Moments|Steiner's Theorem]], the following applies generally to the variance of a random variable: $\sigma^2 = m_2 \, &ndash;m_1^2$.  
+
'''(3)'''&nbsp; According to the [[Stochastische_Signaltheorie/Erwartungswerte_und_Momente#Einige_h.C3.A4ufig_benutzte_Zentralmomente| Steiner's Theorem]], the variance of any random variable is $\sigma^2 = m_2 \, &ndash;m_1^2$.  
*According to the above equation, $m_2 = 2 \cdot \tau_0^2$. It follows:
+
*This yields $m_2 = 2 \cdot \tau_0^2$, and therefore
$$\sigma_{\rm V}^2 = m_2 - m_1^2 = 2 \cdot \tau_0^2 - (\tau_0)^2 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}
+
:$$\sigma_{\rm V}^2 = m_2 - m_1^2 = 2 \cdot \tau_0^2 - (\tau_0)^2 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}
   \sigma_{\rm V} = \tau_0 \hspace{0.1cm} \underline {= 1\,{\rm & micro; s}
+
   \sigma_{\rm V} = \tau_0 \hspace{0.1cm} \underline {= 1\,{\rm &micro; s}}
   \hspace{0.05cm}. $$
+
   \hspace{0.05cm}. $$
  
  
  
'''(4)'''&nbsp; ${\it \Phi}_{\rm V}(\tau)$ is identical to $x(t)$ given in the auxiliary equation if $t$ is replaced by $\tau$ and $\lambda$ by $1/\tau_0$.  
+
'''(4)'''&nbsp; ${\it \Phi}_{\rm V}(\tau)$ is identical to $x(t)$ in the given Fourier transform pair if $t$ is replaced by $\tau$ and $\lambda$ by $1/\tau_0$.  
*Thus $\varphi_{\rm F}(\delta f)$ has the same course as $X(f)$ with the substitution $f &#8594; \delta f$:
+
*Thus, $\varphi_{\rm F}(\delta f)$ is equal to $X(f)$ with the substitution $f &#8594; \delta f$:
$$\varphi_{\rm F}(\delta f) = \frac{1}{1/\tau_0 + {\rm j} \cdot 2\pi \delta f}
+
:$$\varphi_{\rm F}(\Delta f) = \frac{1}{1/\tau_0 + {\rm j} \cdot 2\pi \Delta f}
  = \frac{\tau_0}{1 + {\rm j} \cdot 2\pi \cdot \tau_0 \cdot \delta f}\hspace{0.05cm}.$
+
  = \frac{\tau_0}{1 + {\rm j} \cdot 2\pi \cdot \tau_0 \cdot \Delta f}\hspace{0.05cm}.$$
  
*"Correct is the <u>first equation.
+
*The <u>first expression</u> is correct.
  
  
  
 
'''(5)'''&nbsp; The coherence bandwidth is implicit in the following equation:
 
'''(5)'''&nbsp; The coherence bandwidth is implicit in the following equation:
$$$|\varphi_{\rm F}(\Delta f = B_{\rm K})| \stackrel {!}{=} \frac{1}{2} \cdot |\varphi_{\rm F}(\delta f = 0)| = \frac{\tau_0}{2}\hspace{0.3cm}
+
:$$|\varphi_{\rm F}(\Delta f = B_{\rm K})| \stackrel {!}{=} \frac{1}{2} \cdot |\varphi_{\rm F}(\Delta f = 0)| = \frac{\tau_0}{2}\hspace{0.3cm}
 
\Rightarrow \hspace{0.3cm}|\varphi_{\rm F}(\Delta f = B_{\rm K})|^2  
 
\Rightarrow \hspace{0.3cm}|\varphi_{\rm F}(\Delta f = B_{\rm K})|^2  
  = \frac{\dot_0^2}{1 + (2\pi \cdot \dot_0 \cdot B_{\rm K})^2} \stackrel {!}{=} \frac{\tau_0^2}{4}$$
+
  = \frac{\tau_0^2}{1 + (2\pi \cdot \tau_0 \cdot B_{\rm K})^2} \stackrel {!}{=} \frac{\tau_0^2}{4}$$
$$\Rightarrow \hspace{0.3cm}(2\pi \cdot \tau_0 \cdot B_{\rm K})^2 = 3 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}
+
:$$\Rightarrow \hspace{0.3cm}(2\pi \cdot \tau_0 \cdot B_{\rm K})^2 = 3 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}
  B_{\rm K}= \frac{\sqrt{3}}{2\pi \cdot \tau_0} \approx \frac{0.276}{ \tau_0}\hspace{0.05cm}. $
+
  B_{\rm K}= \frac{\sqrt{3}}{2\pi \cdot \tau_0} \approx \frac{0.276}{ \tau_0}\hspace{0.05cm}. $$
  
*With $\tau_0 = 1 \ \ \rm &micro; s$ follows for the coherence bandwidth $B_{\rm K} \ \underline {= 276 \ \ \rm kHz}$.
+
*With $\tau_0 = 1 \ \ \rm &micro; s$, the coherence bandwidth is $B_{\rm K} \ \underline {= 276 \ \ \rm kHz}$.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
 
[[Category:Exercises for Mobile Communications|^2.3 The GWSSUS Channel Model^]]
 
[[Category:Exercises for Mobile Communications|^2.3 The GWSSUS Channel Model^]]

Revision as of 18:39, 22 April 2020

Verzögerungs–LDS und
Frequenz–Korrelationsfunktion

For the power spectral density of the delay, we assume an exponential behavior. With  ${\it \Phi}_0 = {\it \Phi}_{\rm V}(\tau = 0)$  we have

$${{\it \phi}_{\rm V}(\tau)}/{{\it \phi}_{\rm 0}} = {\rm e}^{ -\tau / \tau_0 } \hspace{0.05cm}.$$

The constant  $\tau_0$  can be determined from the tangent in the point  $\tau = 0$  according to the upper graph. Note that  ${\it \Phi}_{\rm V}(\tau)$  has dimension  $[1/\rm s]$ . Furthermore,

  • The probability density function (PDF)  $f_{\rm V}(\tau)$  has the same form as  ${\it \Phi}_{\rm V}(\tau)$, but is normalized to area  $1$ .
  • The  average excess delay or mean excess delay  $m_{\rm V}$  is equal to the linear expectation  $E\big [\tau \big]$  and can be determined from the PDF  $f_{\rm V}(\tau)$ .
  • The  multipath spread or delay spread  $\sigma_{\rm V}$  gives the standard deviation (dispersion) of the random variable  $\tau$ . In the theory part we also use the term  $T_{\rm V}$ for this.
  • The displayed frequency correlation function  $\varphi_{\rm F}(\delta f)$  can be calculated as the Fourier transform of the power spectral density of the delay  ${\it \Phi}_{\rm V}(\tau)$ :
$$\varphi_{\rm F}(\Delta f) \hspace{0.2cm} {\bullet\!\!-\!\!\!-\!\!\!-\!\!\circ} \hspace{0.2cm} {\it \Phi}_{\rm V}(\tau)\hspace{0.05cm}.$$
  • The coherence bandwidth  $B_{\rm K}$  is the value of   $\Delta f$ at which the frequency correlation function  $\varphi_{\rm F}(\Delta f)$  has dropped to half in absolute value.




Notes:

  • This task belongs to the topic of the chapter  GWSSUS–Kanalmodell.
  • This task requires knowledge of   computation of moments  of random variables from the book „Stochastic Signal Theory”.
  • In addition, the following Fourier transform is given:
$$x(t) = \left\{ \begin{array}{c} {\rm e}^{- \lambda \hspace{0.05cm}\cdot \hspace{0.05cm} t}\\ 0 \end{array} \right.\quad \begin{array}{*{1}c} \hspace{-0.35cm} {\rm f\ddot{u}r} \hspace{0.15cm} t \ge 0 \\ \hspace{-0.35cm} {\rm f\ddot{u}r} \hspace{0.15cm} t < 0 \\ \end{array} \hspace{0.4cm} {\circ\!\!-\!\!\!-\!\!\!-\!\!\bullet} \hspace{0.4cm} X(f) = \frac{1}{\lambda + {\rm j} \cdot 2\pi f}\hspace{0.05cm}.$$



Questionnaire

1

What is the probability density  $f_{\rm V}(\tau)$  of the delay?

$f_{\rm V}(\tau) = {\rm e}^{-\tau/\tau_0}$.
$f_{\rm V}(\tau) = 1/\tau_0 \cdot {\rm e}^{-\tau/\tau_0}$,
$f_{\rm V}(\tau) = {\it \Phi}_0 \cdot {\rm e}^{-\tau/\tau_0}$.

2

Determine the average delay time for  $\tau_0 = 1 \ \ \rm µ s$.

$m_{\rm V} \ = \ $

$\ \rm µ s$

3

Which value results for the multipath widening with  $\tau_0 = 1 \ \ \rm µ s$?

$\sigma_{\rm V} \ = \ $

$\ \rm µ s$

4

What is the frequency–correlation function  $\varphi_{\rm F}(\Delta f)$?

$\varphi_{\rm F}(\Delta f) = \big[1/\tau_0 + {\rm j} \ 2 \pi \cdot \delta f \big]^{-1}$,
$\varphi_{\rm F}(\Delta f) = {\rm e}^ {-(\tau_0 \hspace{0.05cm}\cdot \hspace{0.05cm}\Delta f)^2}$.

5

Determine the coherence bandwidth  $B_{\rm K}$.

$B_{\rm K} \ = \ $

$\ \ \rm kHz$


Sample solution

(1)  Let ${\it \Phi}_0 = {\it \Phi}_{\rm V}(\tau = 0)$. The integral of the power spectral density of the delay gives

$$\int_{0}^{+\infty} {\it \Phi}_{\rm V}(\tau) \hspace{0.15cm}{\rm d} \tau = {\it \Phi}_{\rm 0} \cdot \int_{0}^{+\infty} {\rm e}^{-\tau / \tau_0} \hspace{0.15cm}{\rm d} \tau = {\it \Phi}_{\rm 0} \cdot \tau_0 \hspace{0.05cm}. $$
  • The probability density function is then
$$f_{\rm V}(\tau) = \frac{{\it \Phi}_{\rm V}(\tau) }{{\it \Phi}_{\rm 0} \cdot \tau_0}= \frac{1}{\tau_0} \cdot {\rm e}^{-\tau / \tau_0} \hspace{0.05cm}.$$


  • Solution 2 is therefore correct.


(2)  The $k$-th moment of an exponential random variable is $m_k = k! \cdot \tau_0^k$.

  • With $k = 1$, this results in the linear mean value $m_1 = m_{\rm V}$:
$$m_{\rm V} = \tau_0 \hspace{0.1cm} \underline {= 1\,{\rm µ s}} \hspace{0.05cm}. $$

(3)  According to the Steiner's Theorem, the variance of any random variable is $\sigma^2 = m_2 \, –m_1^2$.

  • This yields $m_2 = 2 \cdot \tau_0^2$, and therefore
$$\sigma_{\rm V}^2 = m_2 - m_1^2 = 2 \cdot \tau_0^2 - (\tau_0)^2 \hspace{0.3cm} \Rightarrow \hspace{0.3cm} \sigma_{\rm V} = \tau_0 \hspace{0.1cm} \underline {= 1\,{\rm µ s}} \hspace{0.05cm}. $$


(4)  ${\it \Phi}_{\rm V}(\tau)$ is identical to $x(t)$ in the given Fourier transform pair if $t$ is replaced by $\tau$ and $\lambda$ by $1/\tau_0$.

  • Thus, $\varphi_{\rm F}(\delta f)$ is equal to $X(f)$ with the substitution $f → \delta f$:
$$\varphi_{\rm F}(\Delta f) = \frac{1}{1/\tau_0 + {\rm j} \cdot 2\pi \Delta f} = \frac{\tau_0}{1 + {\rm j} \cdot 2\pi \cdot \tau_0 \cdot \Delta f}\hspace{0.05cm}.$$
  • The first expression is correct.


(5)  The coherence bandwidth is implicit in the following equation:

$$|\varphi_{\rm F}(\Delta f = B_{\rm K})| \stackrel {!}{=} \frac{1}{2} \cdot |\varphi_{\rm F}(\Delta f = 0)| = \frac{\tau_0}{2}\hspace{0.3cm} \Rightarrow \hspace{0.3cm}|\varphi_{\rm F}(\Delta f = B_{\rm K})|^2 = \frac{\tau_0^2}{1 + (2\pi \cdot \tau_0 \cdot B_{\rm K})^2} \stackrel {!}{=} \frac{\tau_0^2}{4}$$
$$\Rightarrow \hspace{0.3cm}(2\pi \cdot \tau_0 \cdot B_{\rm K})^2 = 3 \hspace{0.3cm} \Rightarrow \hspace{0.3cm} B_{\rm K}= \frac{\sqrt{3}}{2\pi \cdot \tau_0} \approx \frac{0.276}{ \tau_0}\hspace{0.05cm}. $$
  • With $\tau_0 = 1 \ \ \rm µ s$, the coherence bandwidth is $B_{\rm K} \ \underline {= 276 \ \ \rm kHz}$.