Aufgaben:Exercise 3.7Z: Error Performance: Difference between revisions
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{{quiz-Header|Buchseite= | {{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Gaussian_Distributed_Random_Variables | ||
}} | }} | ||
[[File:P_ID132__Sto_Z_3_7.png|right|frame< | [[File:P_ID132__Sto_Z_3_7.png|right|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 [https://de.wikipedia.org/wiki/G.821 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: | ||
:$$\rm Pr(\it f \le \rm 64) \ge \rm 0.998.$$ | :$$\rm Pr(\it f \le \rm 64) \ge \rm 0.998.$$ | ||
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Hints: | |||
*The exercise belongs to the chapter [[Theory_of_Stochastic_Signals/Gaussian_Distributed_Random_Variables|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=== | |||
=== | |||
<quiz display=simple> | <quiz display=simple> | ||
{ | {Which of the following statements are true regarding the random variable $f$ ? | ||
|type="[]"} | |type="[]"} | ||
+ | + The random variable $f$ is binomially distributed. | ||
+ $f$ | + $f$ can be approximated by a Poisson distribution. | ||
{ | {What is the mean value of the random variable $f$? | ||
|type="{}"} | |type="{}"} | ||
$m_f \ = \ $ { 64 3% } | $m_f \ = \ $ { 64 3% } | ||
{ | {How large is the standard deviation? Use appropriate approximations. | ||
|type="{}"} | |type="{}"} | ||
$\sigma_f \ = | $\sigma_f \ = \ $ { 8 3% } | ||
{ | {Calculate the probability that no more than $64$ bit errors occur. Use the Gaussian approximation. | ||
|type="{}"} | |type="{}"} | ||
${\rm Pr}(f ≤ 64) \ = | ${\rm Pr}(f ≤ 64) \ = \ $ { 50 3% } $ \ \rm \%$ | ||
{ | {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? <br>It holds ${\rm Q}(2.9) \approx 0.002$. | ||
|type="{}"} | |type="{}"} | ||
$p_\text{B, max}\ = | $p_\text{B, max}\ = \ $ { 0.069 3% } $ \ \rm \%$ | ||
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</quiz> | </quiz> | ||
=== | ===Solution=== | ||
{{ML-Kopf}} | {{ML-Kopf}} | ||
'''(1)''' <u> | '''(1)''' <u>Both statements</u> 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)''' | '''(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)''' | '''(3)''' For the standard deviation one obtains | ||
:$$\it \sigma_f=\rm\sqrt{\rm 64000\cdot 10^{-3}\cdot 0.999}\hspace{0.15cm}\underline{\approx\sqrt{64}=8}.$$ | :$$\it \sigma_f=\rm\sqrt{\rm 64000\cdot 10^{-3}\cdot 0.999}\hspace{0.15cm}\underline{\approx\sqrt{64}=8}.$$ | ||
* | * The error by applying Poisson distribution instead of binomial distribution here is smaller than $0.05\%$. | ||
'''(4)''' | '''(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)''' | '''(5)''' With $\lambda = N \cdot p$ the corresponding condition is: | ||
:$$\rm Q\big (\frac{\rm 64-\it \lambda}{\sqrt{\it \lambda}} \big )\le \rm | :$$\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.$$ | :$$ \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 | :$$\sqrt{\it \lambda}=\frac{\rm -2.9\pm\rm\sqrt{\rm 8.41+256}}{\rm 2}=\rm 6.68\hspace{0.5cm}\rightarrow \hspace{0.5cm}\lambda = 44.6 \hspace{0.5cm}\Rightarrow \hspace{0.5cm}{\it p}_\text{B, max}= \frac{44.6}{64000} \hspace{0.15cm}\underline{\approx 0.069\%}.$$ | ||
\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. | ||
{{ML-Fuß}} | {{ML-Fuß}} | ||
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[[Category:Theory of Stochastic Signals: Exercises|^3.5 Gaussian Random Variable^]] | [[Category:Theory of Stochastic Signals: Exercises|^3.5 Gaussian Random Variable^]] | ||
[[de:Aufgaben:Aufgabe 3.7Z: Error Performance]] | |||
Latest revision as of 17:54, 16 March 2026

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
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.