Difference between revisions of "Aufgaben:Exercise 1.3Z: Rayleigh Fading Revisited"

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{{quiz-Header|Buchseite=Mobile Kommunikation/Wahrscheinlichkeitsdichte des Rayleigh–Fadings}}
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{{quiz-Header|Buchseite=Mobile_Communications/Probability_Density_of_Rayleigh_Fading}}
  
[[File:P_ID2107__Mob_Z_1_3.png|right|frame|Zwei Kanäle, gekennzeichnet durch komplexen Faktor  $z(t)$]]
+
[[File:EN_Mob_A_1_3Z.png|right|frame|Two channels, characterized by complex factor  $z(t)$]]
The graph shows the multiplicative factor  $z(t) = x(t) + {\rm j} \cdot y(t)$  of two mobile radio channels (both without multipath propagation) in 2D–representation. The following is assumed:
+
The graph shows the multiplicative factor  $z(t) = x(t) + {\rm j} \cdot y(t)$  of two mobile radio channels  (both without multipath propagation)  in 2D–representation.  The following is assumed:
* The channel  $\rm R$  (the designation results from the color „Red” of the point cloud) is Rayleigh distributed with  $\sigma_{\rm R} = 0.5$.
+
* The channel  $\rm R$  $($the designation results from the color "Red" of the point cloud$)$  is Rayleigh distributed with  $\sigma_{\rm R} = 0.5$.
* The probability density functions (PDF) of the magnitude  $a(t) = |z(t)|$  or   the squared magnitude $p(t) = |z(t)|^2$  are $($with  $\sigma = \sigma_{\rm R})$:
+
* The probability density functions&nbsp $\rm (PDF)$  of the magnitude  $a(t) = |z(t)|$  or   the squared magnitude $p(t) = |z(t)|^2$  are $($with  $\sigma = \sigma_{\rm R})$:
 
:$$f_a(a) =
 
:$$f_a(a) =
\left\{ \begin{array}{c} a/\sigma^2 \cdot {\rm e}^{ -{a^2}/(2\sigma^2)} \\\
+
\left\{ \begin{array}{c} a/\sigma^2 \cdot {\rm e}^{ -{a^2}/(2\sigma^2)} \\
0 \end{array} \right.\quad
+
0 \end{array} \right.\quad
\begin{array}{*{*{1}c} {\rm f\ddot{u}r}\hspace{0.15cm} a \ge 0
+
\begin{array}{*{1}c} {\rm for}\hspace{0.15cm} a \ge 0
\\ {\rm f\ddot{u}r}\hspace{0.15cm} a < 0 \\\ \\ \end{array}
+
\\ {\rm for}\hspace{0.15cm} a < 0 \\ \end{array}
 
  \hspace{0.05cm},$$
 
  \hspace{0.05cm},$$
 
:$$f_p(p) =
 
:$$f_p(p) =
\left\{ \begin{array}{c} 1/(2\sigma^2) \cdot {\rm e}^{ -{p}/(2\sigma^2)} \\\
+
\left\{ \begin{array}{c} 1/(2\sigma^2) \cdot {\rm e}^{ -{p}/(2\sigma^2)} \\
0 \end{array} \right.\quad
+
0 \end{array} \right.\quad
\begin{array}{*{*{1}c} {\rm f\ddot{u}r}\hspace{0.15cm} p \ge 0
+
\begin{array}{*{1}c} {\rm for}\hspace{0.15cm} p \ge 0
\\ {\rm f\ddot{u}r}\hspace{0.15cm} p < 0 \\\ \\ \end{array}
+
\\ {\rm for}\hspace{0.15cm} p < 0 \\ \end{array}
 
.$$
 
.$$
* From channel &nbsp;$\rm B$&nbsp; ("Blue") only the point cloud is given. It must be estimated whether <i>Rayleigh&ndash;Fading</i>&nbsp; is also present here, and if YES, how large the parameter&nbsp; $\sigma = \sigma_{\rm B}$&nbsp; is for this channel.
+
* For channel &nbsp;$\rm B$&nbsp; ("Blue") only the point cloud is given.&nbsp It must be estimated whether Rayleigh fading is also present here, and if YES, how large the parameter&nbsp; $\sigma = \sigma_{\rm B}$&nbsp; is for this channel.
* Finally, subtask '''(3)''' also refers to PDF&nbsp; $f_{\it \phi}(\phi)$&nbsp; the phase function&nbsp; $\phi(t)$&nbsp;. This is defined as follows:
+
* Finally, subtask&nbsp; '''(3)'''&nbsp; also refers to the PDF&nbsp; $f_{\it \phi}(\phi)$&nbsp; of the phase function&nbsp; $\phi(t)$&nbsp;, which is defined as follows:
  
$$\phi(t) = \arctan \hspace{0.15cm} \frac{y(t)}{x(t)}
+
:$$\phi(t) = \arctan \hspace{0.15cm} \frac{y(t)}{x(t)}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
Line 29: Line 29:
  
 
''Notes:''  
 
''Notes:''  
* The task belongs to chapter&nbsp; [[Mobile_Communications/Probability Density_of_Rayleigh%E2%80%93Fadings|Probability Density of Rayleigh&ndash;Fadings]] of this book.  
+
* This task belongs to chapter&nbsp; [[Mobile_Communications/Probability_density_of_Rayleigh_fading|Probability density of Rayleigh fading]]&nbsp; of this book.  
* A similar topic is treated with a different approach in chapter&nbsp; [[Stochastic_Signal Theory/Weitere_Verteilungen|Weitere Verteilungen]]&nbsp; of the book &bdquo;Stochastic Signal Theory&rdquo;.
+
* A similar topic is treated with a different approach in chapter&nbsp; [[Theory_of_Stochastic_Signals/Further_distributions|Further distributions]]&nbsp; of the book "Stochastic Signal Theory".
* To check your results you can use the interactive applet&nbsp; [[Applets:WDF_VTF|WDF, VTF and Moments]]&nbsp; of the book &bdquo;Stochastic Signal Theory&rdquo;.
+
* To check your results, you can use the interactive applet&nbsp; [[Applets:PDF,_CDF_and_Moments_of_Special_Distributions|PDF, CDF and Moments of Special Distributions]]&nbsp; of the book "Stochastic Signal Theory".
 
   
 
   
  
  
===Questionnaire===
+
===Questions===
 
<quiz display=simple>
 
<quiz display=simple>
Can the channel &nbsp;$\rm B$&nbsp; also be modeled by &bdquo;<i>Rayleigh</i>&rdquo;?
+
{Can the channel &nbsp;$\rm B$&nbsp; also be modeled as Rayleigh fading?
 
|type="()"}
 
|type="()"}
 
+ Yes.
 
+ Yes.
 
- No.
 
- No.
  
Estimate the Rayleigh&ndash parameter of channel $\rm B$&nbsp. Reminder: &nbsp; For channel &nbsp;$\rm R$&nbsp; this parameter has the value&nbsp; $\sigma_{\rm R} = 0.5$.
+
{Estimate the Rayleigh parameter of channel&nbsp; $\rm B$.&nbsp; Reminder: &nbsp; For channel &nbsp;$\rm R$&nbsp; this parameter has the value&nbsp; $\sigma_{\rm R} = 0.5$.
 
|type="{}"}
 
|type="{}"}
 
$\sigma_{\rm B}\ = \ $ { 0.707 3% }  
 
$\sigma_{\rm B}\ = \ $ { 0.707 3% }  
  
{Do the phases&ndash;probability density functions&nbsp; $f_{\it \phi}(\phi)$ differ from channel &nbsp;$\rm R$&nbsp; and &nbsp;$\rm B$&nbsp; and if YES, how?
+
{Are the probability density functions&nbsp; (PDFs)&nbsp; $f_{\it \phi}(\phi)$&nbsp; of the phases different for channel &nbsp;$\rm R$&nbsp; and &nbsp;$\rm B$&nbsp; and if &nbsp; YES, &nbsp; how?
 
|type="()"}
 
|type="()"}
- Yeah.
+
- Yes.
 
+ No.
 
+ No.
  
{What is the course of WDF&nbsp; $f_a(a)$&nbsp; with&nbsp; $a(t) = |z(t)|$ in both cases?
+
{What is the PDF&nbsp; $f_a(a)$&nbsp; with&nbsp; $a(t) = |z(t)|$&nbsp; in both cases?
 
|type="()"}
 
|type="()"}
- The amount&nbsp; $a(t)$&nbsp; is gaussian distributed.
+
- The random variable&nbsp; $a(t)$&nbsp; is Gaussian distributed.
+ The amount&nbsp; $a(t)$&nbsp; is rayleigh distributed.
+
+ The random variable&nbsp; $a(t)$&nbsp; is Rayleigh distributed.
- The amount&nbsp; $a(t)$&nbsp; is positive&ndash;exponentially distributed.
+
- The random variable&nbsp; $a(t)$&nbsp; is positive and exponentially distributed.
  
{What is the course of the WDF&nbsp; $f_p(p)$&nbsp; in both cases with &nbsp;$p(t) = |z(t)|^2$?
+
{What is the PDF&nbsp; $f_p(p)$&nbsp; in both cases with &nbsp;$p(t) = |z(t)|^2$?
 
|type="()"}
 
|type="()"}
- The amount&nbsp; $p(t)$&nbsp; is gaussian distributed.
+
- The random variable&nbsp; $p(t)$&nbsp; is Gaussian distributed.
- The amount&nbsp; $p(t)$&nbsp; is rayleigh distributed.
+
- The random variable&nbsp; $p(t)$&nbsp; is Rayleigh distributed.
+ The amount&nbsp; $p(t)$&nbsp; is positive&ndash;exponentially distributed.
+
+ The random variable&nbsp; $p(t)$&nbsp; is positive and exponentially distributed.
  
{What is the probability that the amount&nbsp; $a(t) = |z(t)|$&nbsp; is greater than a given value&nbsp; $A$?
+
{What is the probability that the random variable&nbsp; $a(t) = |z(t)|$&nbsp; is greater than a given value&nbsp; $A$?
 
|type="()"}
 
|type="()"}
- It applies&nbsp; ${\rm Pr}(|z(t)|) > A) = {\rm ln}(A/\sigma).$
+
- ${\rm Pr}(|z(t)|) > A) = {\rm ln}(A/\sigma).$
+ The following applies&nbsp; ${\rm Pr}(|z(t)|) > A) = {\rm e}^{-A^2/2\sigma^2}.$
+
+ ${\rm Pr}(|z(t)|) > A) = {\rm e}^{-A^2/2\sigma^2}.$
- It applies&nbsp; ${\rm Pr}(|z(t)|) > A) = {\rm e}^{-A/\sigma}.$
+
- ${\rm Pr}(|z(t)|) > A) = {\rm e}^{-A/\sigma}.$
  
 
{Calculate the probability&nbsp; ${\rm Pr}(|z(t)| > 1)$ for both channels.
 
{Calculate the probability&nbsp; ${\rm Pr}(|z(t)| > 1)$ for both channels.
Line 73: Line 73:
 
Channel &nbsp;$\rm R$:$\hspace{0.4cm} {\rm Pr}(|z(t)| > 1)\ = \ $ { 0.135 3% }
 
Channel &nbsp;$\rm R$:$\hspace{0.4cm} {\rm Pr}(|z(t)| > 1)\ = \ $ { 0.135 3% }
 
Channel &nbsp;$\rm B$:$\hspace{0.4cm} {\rm Pr}(|z(t)| > 1)\ = \ $ { 0.368 3% }
 
Channel &nbsp;$\rm B$:$\hspace{0.4cm} {\rm Pr}(|z(t)| > 1)\ = \ $ { 0.368 3% }
</quiz
+
</quiz>
  
===Sample solution===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; <u>correct is YES</u>:  
+
'''(1)'''&nbsp; <u>YES</u>:  
*You can see the rotational symmetry here, too, if you consider that only $N = 10\hspace{0.05cm}000$ samples were displayed in the complex plane.  
+
*You can see the rotational symmetry here, too, if you consider that only&nbsp; $N = 10\hspace{0.05cm}000$&nbsp; samples were displayed in the complex plane.  
*Furthermore the following questions would not make sense if you answer NO.
+
*Furthermore the following questions would not make sense if you answer "NO".
  
  
  
'''(2)'''&nbsp; By measuring the two drawn circles you can see that for the &bdquo;blue&rdquo; channel the scattering of real&ndash; and imaginary part are larger by a factor of about 1.4 (exactly: $\sqrt{2}$) than for the &bdquo;red&rdquo; channel:
+
'''(2)'''&nbsp; By measuring the two drawn circles you can see that for the "blue" channel the scattering of real&ndash; and imaginary part are larger by a factor of about&nbsp; $1.4$&nbsp; <br>$($exactly:&nbsp; $\sqrt{2})$&nbsp; than for the "red" channel:
 
$$\sigma_{\rm B} = \sigma_{\rm R} \cdot \sqrt{2} = 0.5 \cdot \sqrt{2}= {1}/{\sqrt{2}}\hspace{0.15cm} \underline{\approx 0.707}
 
$$\sigma_{\rm B} = \sigma_{\rm R} \cdot \sqrt{2} = 0.5 \cdot \sqrt{2}= {1}/{\sqrt{2}}\hspace{0.15cm} \underline{\approx 0.707}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
  
'''(3)'''&nbsp; <u>correct is NO</u>:  
+
'''(3)'''&nbsp; <u>NO</u>:  
*In both cases $f_{\it \phi}(\phi)$ describes an equal distribution between $-\pi$ and $+\pi$.  
+
*In both cases&nbsp; $f_{\it \phi}(\phi)$&nbsp; describes a uniform distribution between&nbsp; $-\pi$&nbsp; and&nbsp; $+\pi$.  
*The larger amplitudes of channel &nbsp;$\rm B$&nbsp; are not important for the phase function $\phi(t)$.
+
*The larger amplitudes of channel&nbsp; &nbsp;$\rm B$&nbsp; are not important for the phase function&nbsp; $\phi(t)$.
  
  
  
'''(4)'''&nbsp; Correct is the <u>solution 2</u>:  
+
'''(4)'''&nbsp; <u>Solution 2</u> is correct:  
*With Rayleigh&ndash;Fading, the real part $x(t)$ and the imaginary part $y(t)$ are each Gaussian distributed.  
+
*With Rayleigh fading, the real part&nbsp; $x(t)$&nbsp; and the imaginary part&nbsp; $y(t)$&nbsp; are each Gaussian distributed.  
*The exponential distribution results for the square of the absolute value $p(t) = |z(t)|^2$.
+
*The exponential distribution results for the square of the absolute value&nbsp; $p(t) = |z(t)|^2$.
  
  
  
'''(5)'''&nbsp; Correct here is the <u>solution 3</u>, as already explained in the sample solution for '''(4)'''.
+
'''(5)'''&nbsp; <u>Solution 3</u> is correct, as already explained in the sample solution for&nbsp; '''(4)'''.
  
  
  
'''(6)'''&nbsp; The amount $a(t)$ is rayleigh distributed. Therefore, the following applies for the searched probability:
+
'''(6)'''&nbsp; The magnitude&nbsp; $a(t)$ &nbsp;is Rayleigh distributed.&nbsp; Therefore, the following holds for the desired probability:
$${\rm Pr}(a > A) = \int_{A}^{\infty}\frac{a}{\sigma^2} \cdot {\rm e}^{ -{a^2}/(2\sigma^2)} \hspace{0.15cm}{\rm d}a \hspace{0.05cm}.$
+
:$${\rm Pr}(a > A) = \int_{A}^{\infty}\frac{a}{\sigma^2} \cdot {\rm e}^{ -{a^2}/(2\sigma^2)} \hspace{0.15cm}{\rm d}a \hspace{0.05cm}.$$
  
 
*In some formula collections you can find the solution for this integral, but not in all.  
 
*In some formula collections you can find the solution for this integral, but not in all.  
*But it is also valid with the one-sided&ndash;exponentially distributed random variable $p = a^2$:
+
*But it is also valid with the one-sided exponentially distributed random variable&nbsp; $p = a^2$:
$${\rm Pr}(a > A) = {\rm Pr}(p > A^2) = \frac{1}{2\sigma^2} \cdot\int_{A^2}^{\infty} {\rm e}^{ -{p}/(2\sigma^2)} \hspace{0.15cm}{\rm d}p \hspace{0.05cm}.$$
+
:$${\rm Pr}(a > A) = {\rm Pr}(p > A^2) = \frac{1}{2\sigma^2} \cdot\int_{A^2}^{\infty} {\rm e}^{ -{p}/(2\sigma^2)} \hspace{0.15cm}{\rm d}p \hspace{0.05cm}.$$
  
 
*This integral is elementary and gives the result:
 
*This integral is elementary and gives the result:
$${\rm Pr}(a > A) = {\rm e}^{ -{A^2}/(2\sigma^2)} \hspace{0.05cm}.$$
+
:$${\rm Pr}(a > A) = {\rm e}^{ -{A^2}/(2\sigma^2)} \hspace{0.05cm}.$$
  
Correct is therefore the <u>solution 2</u>.
+
The correct answer is therefore <u>solution 2</u>.
  
  
  
'''(7)'''&nbsp; For the channel &nbsp;$\rm R$&nbsp; applies with $\sigma = 0.5$:
+
'''(7)'''&nbsp; For the channel &nbsp;$\rm R$&nbsp; and $\sigma = 0.5$ &nbsp; &rArr; &nbsp; $\sigma^2 = 0.25$, we have
$${\rm Pr}(|z(t)| > 1) = {\rm e}^{-2} \hspace{0.15cm} \underline{\approx 0.135}
+
:$${\rm Pr}(|z(t)| > 1) = {\rm e}^{-2} \hspace{0.15cm} \underline{\approx 0.135}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
  
*In the upper graph this corresponds to the number of all points outside the circle drawn, based on the number $N = $10,000$ of all points.
+
*In the upper graph this corresponds to the number of all points outside the circle drawn, based on the number&nbsp; $N = 10,000$&nbsp; of all points.
  
*For the channel &nbsp;$\rm B$&nbsp; because of the double variance $\sigma^2 = 0.5$, ${\rm Pr}(|z(t)|>1) = {\rm e}^{\rm &ndash;1} applies. \ \underline {\approx \ 0.368}$.  
+
*For the channel &nbsp;$\rm B$&nbsp; because of the double variance&nbsp; $\sigma^2 = 0.5$, we have&nbsp; ${\rm Pr}(|z(t)|>1) = {\rm e}^{-1} \ \underline {\approx \ 0.368}$.  
  
*The (not drawn) reference circle would also have the radius 1 in the lower graph.  
+
*The (not drawn) reference circle would also have radius&nbsp; $1$&nbsp; in the bottom graph.  
*The circle drawn in the lower graphic has a larger radius than $A = $1, namely $A = \sqrt{2}\approx $1,414.
+
*The circle drawn in the bottom graph has a larger radius than&nbsp; $A = 1$,&nbsp; namely&nbsp; $A = \sqrt{2}\approx 1.414$.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
[[Category:Exercises for Mobile Communications|^1.2 PDF of Rayleigh Fading^]]
+
[[Category:Mobile Communications: Exercises|^1.2 PDF of Rayleigh Fading^]]

Latest revision as of 15:43, 28 May 2021

Two channels, characterized by complex factor  $z(t)$

The graph shows the multiplicative factor  $z(t) = x(t) + {\rm j} \cdot y(t)$  of two mobile radio channels  (both without multipath propagation)  in 2D–representation.  The following is assumed:

  • The channel  $\rm R$  $($the designation results from the color "Red" of the point cloud$)$  is Rayleigh distributed with  $\sigma_{\rm R} = 0.5$.
  • The probability density functions&nbsp $\rm (PDF)$  of the magnitude  $a(t) = |z(t)|$  or   the squared magnitude $p(t) = |z(t)|^2$  are $($with  $\sigma = \sigma_{\rm R})$:
$$f_a(a) = \left\{ \begin{array}{c} a/\sigma^2 \cdot {\rm e}^{ -{a^2}/(2\sigma^2)} \\ 0 \end{array} \right.\quad \begin{array}{*{1}c} {\rm for}\hspace{0.15cm} a \ge 0 \\ {\rm for}\hspace{0.15cm} a < 0 \\ \end{array} \hspace{0.05cm},$$
$$f_p(p) = \left\{ \begin{array}{c} 1/(2\sigma^2) \cdot {\rm e}^{ -{p}/(2\sigma^2)} \\ 0 \end{array} \right.\quad \begin{array}{*{1}c} {\rm for}\hspace{0.15cm} p \ge 0 \\ {\rm for}\hspace{0.15cm} p < 0 \\ \end{array} .$$
  • For channel  $\rm B$  ("Blue") only the point cloud is given.&nbsp It must be estimated whether Rayleigh fading is also present here, and if YES, how large the parameter  $\sigma = \sigma_{\rm B}$  is for this channel.
  • Finally, subtask  (3)  also refers to the PDF  $f_{\it \phi}(\phi)$  of the phase function  $\phi(t)$ , which is defined as follows:
$$\phi(t) = \arctan \hspace{0.15cm} \frac{y(t)}{x(t)} \hspace{0.05cm}.$$




Notes:


Questions

1

Can the channel  $\rm B$  also be modeled as Rayleigh fading?

Yes.
No.

2

Estimate the Rayleigh parameter of channel  $\rm B$.  Reminder:   For channel  $\rm R$  this parameter has the value  $\sigma_{\rm R} = 0.5$.

$\sigma_{\rm B}\ = \ $

3

Are the probability density functions  (PDFs)  $f_{\it \phi}(\phi)$  of the phases different for channel  $\rm R$  and  $\rm B$  and if   YES,   how?

Yes.
No.

4

What is the PDF  $f_a(a)$  with  $a(t) = |z(t)|$  in both cases?

The random variable  $a(t)$  is Gaussian distributed.
The random variable  $a(t)$  is Rayleigh distributed.
The random variable  $a(t)$  is positive and exponentially distributed.

5

What is the PDF  $f_p(p)$  in both cases with  $p(t) = |z(t)|^2$?

The random variable  $p(t)$  is Gaussian distributed.
The random variable  $p(t)$  is Rayleigh distributed.
The random variable  $p(t)$  is positive and exponentially distributed.

6

What is the probability that the random variable  $a(t) = |z(t)|$  is greater than a given value  $A$?

${\rm Pr}(|z(t)|) > A) = {\rm ln}(A/\sigma).$
${\rm Pr}(|z(t)|) > A) = {\rm e}^{-A^2/2\sigma^2}.$
${\rm Pr}(|z(t)|) > A) = {\rm e}^{-A/\sigma}.$

7

Calculate the probability  ${\rm Pr}(|z(t)| > 1)$ for both channels.

Channel  $\rm R$:$\hspace{0.4cm} {\rm Pr}(|z(t)| > 1)\ = \ $

Channel  $\rm B$:$\hspace{0.4cm} {\rm Pr}(|z(t)| > 1)\ = \ $


Solution

(1)  YES:

  • You can see the rotational symmetry here, too, if you consider that only  $N = 10\hspace{0.05cm}000$  samples were displayed in the complex plane.
  • Furthermore the following questions would not make sense if you answer "NO".


(2)  By measuring the two drawn circles you can see that for the "blue" channel the scattering of real– and imaginary part are larger by a factor of about  $1.4$ 
$($exactly:  $\sqrt{2})$  than for the "red" channel: $$\sigma_{\rm B} = \sigma_{\rm R} \cdot \sqrt{2} = 0.5 \cdot \sqrt{2}= {1}/{\sqrt{2}}\hspace{0.15cm} \underline{\approx 0.707} \hspace{0.05cm}.$$


(3)  NO:

  • In both cases  $f_{\it \phi}(\phi)$  describes a uniform distribution between  $-\pi$  and  $+\pi$.
  • The larger amplitudes of channel   $\rm B$  are not important for the phase function  $\phi(t)$.


(4)  Solution 2 is correct:

  • With Rayleigh fading, the real part  $x(t)$  and the imaginary part  $y(t)$  are each Gaussian distributed.
  • The exponential distribution results for the square of the absolute value  $p(t) = |z(t)|^2$.


(5)  Solution 3 is correct, as already explained in the sample solution for  (4).


(6)  The magnitude  $a(t)$  is Rayleigh distributed.  Therefore, the following holds for the desired probability:

$${\rm Pr}(a > A) = \int_{A}^{\infty}\frac{a}{\sigma^2} \cdot {\rm e}^{ -{a^2}/(2\sigma^2)} \hspace{0.15cm}{\rm d}a \hspace{0.05cm}.$$
  • In some formula collections you can find the solution for this integral, but not in all.
  • But it is also valid with the one-sided exponentially distributed random variable  $p = a^2$:
$${\rm Pr}(a > A) = {\rm Pr}(p > A^2) = \frac{1}{2\sigma^2} \cdot\int_{A^2}^{\infty} {\rm e}^{ -{p}/(2\sigma^2)} \hspace{0.15cm}{\rm d}p \hspace{0.05cm}.$$
  • This integral is elementary and gives the result:
$${\rm Pr}(a > A) = {\rm e}^{ -{A^2}/(2\sigma^2)} \hspace{0.05cm}.$$

The correct answer is therefore solution 2.


(7)  For the channel  $\rm R$  and $\sigma = 0.5$   ⇒   $\sigma^2 = 0.25$, we have

$${\rm Pr}(|z(t)| > 1) = {\rm e}^{-2} \hspace{0.15cm} \underline{\approx 0.135} \hspace{0.05cm}.$$
  • In the upper graph this corresponds to the number of all points outside the circle drawn, based on the number  $N = 10,000$  of all points.
  • For the channel  $\rm B$  because of the double variance  $\sigma^2 = 0.5$, we have  ${\rm Pr}(|z(t)|>1) = {\rm e}^{-1} \ \underline {\approx \ 0.368}$.
  • The (not drawn) reference circle would also have radius  $1$  in the bottom graph.
  • The circle drawn in the bottom graph has a larger radius than  $A = 1$,  namely  $A = \sqrt{2}\approx 1.414$.