Difference between revisions of "Aufgaben:Exercise 3.7Z: Error Performance"

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{{quiz-Header|Buchseite=Stochastische Signaltheorie/Gaußverteilte Zufallsgröße
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{{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Gaussian_Distributed_Random_Variables
 
}}
 
}}
  
[[File:P_ID132__Sto_Z_3_7.png|right|frame<Auszug aus der CCITT-Empfehlung G.821: Error Performance]]
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[[File:P_ID132__Sto_Z_3_7.png|right|frame<excerpt from CCITT Recommendation G.821: Error Performance]]
Jeder Betreiber von ISDN-Systemen muss gewisse Mindestanforderungen hinsichtlich der Bitfehlerquote (BER) einhalten, die zum Beispiel in der&nbsp; [https://de.wikipedia.org/wiki/G.821 CCITT-Empfehlung G.821]&nbsp; unter dem Namen "Error Performance" spezifiziert sind.
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Every operator of ISDN systems must comply with certain minimum requirements regarding the bit error rate&nbsp; $\rm (BER)$,&nbsp; which are specified for example in the&nbsp; [https://de.wikipedia.org/wiki/G.821 CCITT Recommendation G.821]&nbsp; under the name&nbsp; "Error Performance".
  
Rechts sehen Sie einen Auszug aus dieser Empfehlung:  
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On the right you can see an excerpt from this recommendation:  
*Diese besagt unter Anderem,&nbsp; dass &ndash; &uuml;ber eine ausreichend lange Zeit gemittelt &ndash; mindestens&nbsp; $99.8\%$&nbsp; aller Einsekunden-Intervalle eine Bitfehlerquote kleiner als&nbsp; $10^{-3}$&nbsp; (ein Promille) aufweisen m&uuml;ssen.
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*This states,&nbsp; among other things,&nbsp; that &ndash; averaged over a sufficiently long time &ndash; at least&nbsp; $99.8\%$&nbsp; of all one-second intervals must have a bit error rate less than&nbsp; $10^{-3}$&nbsp; (one per thousand).
*Bei einer Bitrate von&nbsp; $\text{64 kbit/s}$&nbsp; entspricht dies der Bedingung, dass in einer Sekunde&nbsp; $($und somit bei&nbsp; $N = 64\hspace{0.08cm}000$&nbsp; &uuml;bertragenen Symbolen$)$&nbsp; nicht mehr als&nbsp; $64$&nbsp; Bitfehler auftreten dürfen:
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*For a bit rate of&nbsp; $\text{64 kbit/s}$&nbsp; this corresponds to the condition that in one second&nbsp; $($and thus for&nbsp; $N = 64\hspace{0.08cm}000$&nbsp; transmitted symbols$)$&nbsp; no more than&nbsp; $64$&nbsp; bit errors may occur:
 
:$$\rm Pr(\it f \le \rm 64) \ge \rm 0.998.$$
 
:$$\rm Pr(\it f \le \rm 64) \ge \rm 0.998.$$
  
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 +
Hints:
 +
*The exercise belongs to the chapter&nbsp; [[Theory_of_Stochastic_Signals/Gaussian_Distributed_Random_Variables|Gaussian distributed random variables]].
 +
*Always assume bit error probability&nbsp; $p = 10^{-3}$&nbsp; for the first three subtasks.
 +
*In addition,&nbsp; throughout the task,&nbsp; let $N = 64\hspace{0.08cm}000$&nbsp; hold.
 +
* Under certain conditions &ndash; which are all fulfilled here &ndash; the binomial distribution can be approximated by a Gaussian distribution with equal mean and equal standard deviation.&nbsp; Use this approximation for the subtask '''(4)'''.
  
  
  
''Hinweise:''
 
*Die Aufgabe gehört zum  Kapitel&nbsp; [[Theory_of_Stochastic_Signals/Gaußverteilte_Zufallsgröße|Gaußverteilte Zufallsgrößen]].
 
 
*Gehen Sie f&uuml;r die ersten drei Teilaufgaben stets von der Bitfehlerwahrscheinlichkeit&nbsp; $p = 10^{-3}$&nbsp; aus.
 
*In der gesamten Aufgabe gelte zudem  $N = 64\hspace{0.08cm}000$.
 
* Unter gewissen Bedingungen &ndash; die hier alle erf&uuml;llt sind &ndash; kann die Binomialverteilung durch eine Gau&szlig;verteilung mit gleichem Mittelwert und gleicher Streuung approximiert werden kann.
 
*Verwenden Sie diese N&auml;herung bei der Teilaufgabe&nbsp; '''(4)'''.
 
  
  
 
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===Questions===
 
 
===Fragebogen===
 
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche der folgenden Aussagen treffen hinsichtlich der Zufallsgr&ouml;&szlig;e $f$&nbsp; zu?
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{Which of the following statements are true regarding the random variable $f$&nbsp;?
 
|type="[]"}
 
|type="[]"}
+ Die Zufallsgr&ouml;&szlig;e $f$&nbsp; ist binomialverteilt.
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+ The random variable $f$&nbsp; is binomially distributed.
+ $f$&nbsp; kann durch eine Poissonverteilung angen&auml;hert werden.
+
+ $f$&nbsp; can be approximated by a Poisson distribution.
  
  
{Welcher Mittelwert ergibt sich f&uuml;r die Zufallsgr&ouml;&szlig;e $f$?
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{What is the mean value of the random variable&nbsp; $f$?
 
|type="{}"}
 
|type="{}"}
 
$m_f \ = \ $ { 64 3% }
 
$m_f \ = \ $ { 64 3% }
  
  
{Wie groß ist die Streuung?&nbsp; Verwenden Sie geeignete N&auml;herungen.
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{How large is the standard deviation?&nbsp; Use appropriate approximations.
 
|type="{}"}
 
|type="{}"}
$\sigma_f \ = \ $ { 8 3% }
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$\sigma_f \ = \ $ { 8 3% }
  
  
{Berechnen Sie Wahrscheinlichkeit, dass nicht mehr als&nbsp; $64$&nbsp; Bitfehler auftreten.&nbsp; Verwenden Sie hierzu die Gau&szlig;n&auml;herung.
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{Calculate the probability that no more than&nbsp; $64$&nbsp; bit errors occur.&nbsp; Use the Gaussian approximation.
 
|type="{}"}
 
|type="{}"}
${\rm Pr}(f ≤ 64) \ = \ $ { 50 3% } $ \ \rm \%$
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${\rm Pr}(f ≤ 64) \ = \ $ { 50 3% } $ \ \rm \%$
  
  
{Wie gro&szlig; darf die Bitfehlerwahrscheinlichkeit&nbsp; $p_\text{B, max}$&nbsp; höchstens sein, damit die Bedingung "64 (oder mehr) Bitfehler nur in höchstens 0.2% der Einsekunden-Intervalle " eingehalten werden kann?&nbsp; Es gilt&nbsp; ${\rm Q}(2.9) \approx 0.002$.
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{What is the maximum bit error probability&nbsp; $p_\text{B, max}$&nbsp; that the condition&nbsp; "64&nbsp; (or more)&nbsp; bit errors only in at most 0.2% of the one-second intervals"&nbsp; can be met?&nbsp; <br>It holds&nbsp; ${\rm Q}(2.9) \approx 0.002$.
 
|type="{}"}
 
|type="{}"}
$p_\text{B, max}\ = \ $ { 0.069 3% } $ \ \rm \%$
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$p_\text{B, max}\ = \ $ { 0.069 3% } $ \ \rm \%$
  
  
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</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; <u>Beide Aussagen</u> sind richtig:  
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'''(1)'''&nbsp; <u>Both statements</u> are correct:  
*Bei der hier definierten Zufallsgröße  $f$&nbsp; handelt es sich um den klassischen Fall einer binomialverteilten Zufallsgr&ouml;&szlig;e:&nbsp; Summe &uuml;ber&nbsp; $N$&nbsp; Bin&auml;rwerte&nbsp; $(0$ oder $1)$.  
+
*The random vairable $f$&nbsp; defined here is the classical case of a binomially distributed random variable:&nbsp; Sum over&nbsp; $N$&nbsp; binary values&nbsp; $(0$ or $1)$.  
*Da das Produkt &nbsp;$N \cdot p = 64$&nbsp; und dadurch sehr viel gr&ouml;&szlig;er als&nbsp; $1$&nbsp; ist,  
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*Because the product &nbsp;$N \cdot p = 64$&nbsp; and thus is much larger than&nbsp; $1$&nbsp;,  
*kann die Binomialverteilung mit guter N&auml;herung durch eine Poissonverteilung mit der Rate&nbsp; ${\it \lambda} = 64$&nbsp; angen&auml;hert werden.
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*the binomial distribution can be approximated with good approximation by a Poisson distribution with rate&nbsp; ${\it \lambda} = 64$&nbsp; .
  
  
  
'''(2)'''&nbsp; Der Mittelwert ergibt sich zu &nbsp;$m_f = N \cdot p \hspace{0.15cm}\underline{= 64}$&nbsp; unabh&auml;ngig davon, ob man von der Binomial&ndash; oder der Poissonverteilung ausgeht.
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'''(2)'''&nbsp; The mean is obtained as &nbsp;$m_f = N \cdot p \hspace{0.15cm}\underline{= 64}$&nbsp; regardless of whether one assumes the binomial distribution or the Poisson distribution.
  
  
  
'''(3)'''&nbsp; F&uuml;r die Streuung erh&auml;lt man &nbsp;  
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'''(3)'''&nbsp; For the standard deviation one obtains &nbsp;  
 
:$$\it \sigma_f=\rm\sqrt{\rm 64000\cdot 10^{-3}\cdot 0.999}\hspace{0.15cm}\underline{\approx\sqrt{64}=8}.$$
 
:$$\it \sigma_f=\rm\sqrt{\rm 64000\cdot 10^{-3}\cdot 0.999}\hspace{0.15cm}\underline{\approx\sqrt{64}=8}.$$
* Der Fehler durch Anwendung der Poissonlverteilung anstelle der Binomialverteilung ist hier kleiner als&nbsp; $0.05\%$.
+
* The error by applying Poisson distribution instead of binomial distribution here is smaller than&nbsp; $0.05\%$.
  
  
  
'''(4)'''&nbsp; Bei einer Gau&szlig;schen Zufallsgr&ouml;&szlig;e $f$&nbsp; mit Mittelwert &nbsp;$m_f {= 64}$&nbsp; ist die Wahrscheinlichkeit&nbsp; ${\rm Pr}(f \le 64) \hspace{0.15cm}\underline{\approx 50\%}$. &nbsp; Anmerkung:  
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'''(4)'''&nbsp; For a Gaussian random variable $f$&nbsp; with mean &nbsp;$m_f {= 64}$&nbsp; the probability&nbsp; ${\rm Pr}(f \le 64) \hspace{0.15cm}\underline{\approx 50\%}$. &nbsp; Note:  
*Bei einer kontinuierlichen Zufallsgr&ouml;&szlig;e w&auml;re die Wahrscheinlichkeit exakt $50\%$.  
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*For a continuous random size,&nbsp; the probability would be exactly $50\%$.  
*Da $f$&nbsp; nur ganzzahlige Werte annehmen kann, ist sie hier geringf&uuml;gig gr&ouml;&szlig;er.
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*Since $f$&nbsp; can only take integer values,&nbsp; it is slightly larger here.
  
  
  
'''(5)'''&nbsp; Mit &nbsp;$\lambda = N \cdot p$&nbsp; lautet die entsprechende Bedingung:
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'''(5)'''&nbsp; With &nbsp;$\lambda = N \cdot p$&nbsp; the corresponding condition is:
:$$\rm Q\big (\frac{\rm 64-\it \lambda}{\sqrt{\it \lambda}} \big )\le \rm 0.002\hspace{0.5cm}\rm bzw.\hspace{0.5cm}\frac{\rm 64-\it \lambda}{\sqrt{\it \lambda}}>\rm 2.9.$$
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:$$\rm Q\big (\frac{\rm 64-\it \lambda}{\sqrt{\it \lambda}} \big )\le \rm 0.002\hspace{0.5cm}\rm or \hspace{0.5cm}\frac{\rm 64-\it \lambda}{\sqrt{\it \lambda}}>\rm 2.9.$$
  
*Der Maximalwert von&nbsp; $\lambda$&nbsp; kann nach folgender Gleichung ermittelt werden:
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*The maximum value of&nbsp; $\lambda$&nbsp; can be determined according to the following equation:
 
:$$ \lambda+\rm 2.9\cdot\sqrt{\it\lambda}-\rm 64 = \rm 0.$$
 
:$$ \lambda+\rm 2.9\cdot\sqrt{\it\lambda}-\rm 64 = \rm 0.$$
  
*Die L&ouml;sung dieser quadratischen Gleichung ist somit:
+
*The solution of this quadratic equation is thus:
 
:$$\sqrt{\it \lambda}=\frac{\rm -2.9\pm\rm\sqrt{\rm 8.41+256}}{\rm 2}=\rm 6.68  
 
:$$\sqrt{\it \lambda}=\frac{\rm -2.9\pm\rm\sqrt{\rm 8.41+256}}{\rm 2}=\rm 6.68  
\hspace{0.5cm}\Rightarrow \hspace{0.5cm}
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\hspace{0.5cm}\rightarrow \hspace{0.5cm}
 
\lambda = 44.6 \hspace{0.5cm}\Rightarrow \hspace{0.5cm}
 
\lambda = 44.6 \hspace{0.5cm}\Rightarrow \hspace{0.5cm}
 
{\it p}_\text{B, max}= \frac{44.6}{64000} \hspace{0.15cm}\underline{\approx 0.069\%}.$$
 
{\it p}_\text{B, max}= \frac{44.6}{64000} \hspace{0.15cm}\underline{\approx 0.069\%}.$$
  
*Die zweite Lösung ist negativ und muss nicht weiter berücksichtigt werden.
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*The second solution is negative and need not be considered further.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[Category:Theory of Stochastic Signals: Exercises|^3.5 Gaußverteilte Zufallsgröße^]]
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[[Category:Theory of Stochastic Signals: Exercises|^3.5 Gaussian Random Variable^]]

Latest revision as of 13:34, 17 February 2022

frame<excerpt from CCITT Recommendation G.821: Error Performance

Every operator of ISDN systems must comply with certain minimum requirements regarding the bit error rate  $\rm (BER)$,  which are specified for example in the  CCITT Recommendation G.821  under the name  "Error Performance".

On the right you can see an excerpt from this recommendation:

  • This states,  among other things,  that – averaged over a sufficiently long time – at least  $99.8\%$  of all one-second intervals must have a bit error rate less than  $10^{-3}$  (one per thousand).
  • For a bit rate of  $\text{64 kbit/s}$  this corresponds to the condition that in one second  $($and thus for  $N = 64\hspace{0.08cm}000$  transmitted symbols$)$  no more than  $64$  bit errors may occur:
$$\rm Pr(\it f \le \rm 64) \ge \rm 0.998.$$



Hints:

  • The exercise belongs to the chapter  Gaussian distributed random variables.
  • Always assume bit error probability  $p = 10^{-3}$  for the first three subtasks.
  • In addition,  throughout the task,  let $N = 64\hspace{0.08cm}000$  hold.
  • Under certain conditions – which are all fulfilled here – the binomial distribution can be approximated by a Gaussian distribution with equal mean and equal standard deviation.  Use this approximation for the subtask (4).



Questions

1

Which of the following statements are true regarding the random variable $f$ ?

The random variable $f$  is binomially distributed.
$f$  can be approximated by a Poisson distribution.

2

What is the mean value of the random variable  $f$?

$m_f \ = \ $

3

How large is the standard deviation?  Use appropriate approximations.

$\sigma_f \ = \ $

4

Calculate the probability that no more than  $64$  bit errors occur.  Use the Gaussian approximation.

${\rm Pr}(f ≤ 64) \ = \ $

$ \ \rm \%$

5

What is the maximum bit error probability  $p_\text{B, max}$  that the condition  "64  (or more)  bit errors only in at most 0.2% of the one-second intervals"  can be met? 
It holds  ${\rm Q}(2.9) \approx 0.002$.

$p_\text{B, max}\ = \ $

$ \ \rm \%$


Solution

(1)  Both statements are correct:

  • The random vairable $f$  defined here is the classical case of a binomially distributed random variable:  Sum over  $N$  binary values  $(0$ or $1)$.
  • Because the product  $N \cdot p = 64$  and thus is much larger than  $1$ ,
  • the binomial distribution can be approximated with good approximation by a Poisson distribution with rate  ${\it \lambda} = 64$  .


(2)  The mean is obtained as  $m_f = N \cdot p \hspace{0.15cm}\underline{= 64}$  regardless of whether one assumes the binomial distribution or the Poisson distribution.


(3)  For the standard deviation one obtains  

$$\it \sigma_f=\rm\sqrt{\rm 64000\cdot 10^{-3}\cdot 0.999}\hspace{0.15cm}\underline{\approx\sqrt{64}=8}.$$
  • The error by applying Poisson distribution instead of binomial distribution here is smaller than  $0.05\%$.


(4)  For a Gaussian random variable $f$  with mean  $m_f {= 64}$  the probability  ${\rm Pr}(f \le 64) \hspace{0.15cm}\underline{\approx 50\%}$.   Note:

  • For a continuous random size,  the probability would be exactly $50\%$.
  • Since $f$  can only take integer values,  it is slightly larger here.


(5)  With  $\lambda = N \cdot p$  the corresponding condition is:

$$\rm Q\big (\frac{\rm 64-\it \lambda}{\sqrt{\it \lambda}} \big )\le \rm 0.002\hspace{0.5cm}\rm or \hspace{0.5cm}\frac{\rm 64-\it \lambda}{\sqrt{\it \lambda}}>\rm 2.9.$$
  • The maximum value of  $\lambda$  can be determined according to the following equation:
$$ \lambda+\rm 2.9\cdot\sqrt{\it\lambda}-\rm 64 = \rm 0.$$
  • The solution of this quadratic equation is thus:
$$\sqrt{\it \lambda}=\frac{\rm -2.9\pm\rm\sqrt{\rm 8.41+256}}{\rm 2}=\rm 6.68 \hspace{0.5cm}\rightarrow \hspace{0.5cm} \lambda = 44.6 \hspace{0.5cm}\Rightarrow \hspace{0.5cm} {\it p}_\text{B, max}= \frac{44.6}{64000} \hspace{0.15cm}\underline{\approx 0.069\%}.$$
  • The second solution is negative and need not be considered further.