Aufgaben:Exercise 5.7Z: McCullough Model once more: Difference between revisions
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{{quiz-Header|Buchseite= | {{quiz-Header|Buchseite=Digital_Signal_Transmission/Burst_Error_Channels}} | ||
[[File:P_ID1845__Dig_Z_5_7.png|right|frame| | [[File:P_ID1845__Dig_Z_5_7.png|right|frame|EDD and ECF of GE model and equivalent MC model]] | ||
As in [[Aufgaben:Exercise_5.6:_Error_Correlation_Duration|"Exercise 5.6"]], [[Aufgaben:Exercise_5.6Z:_Gilbert-Elliott_Model|"Exercise 5.6Z"]] and [[Aufgaben:Exercise_5.7:_McCullough_and_Gilbert-Elliott_Parameters|"Exercise 5.7"]], we consider the burst error channel model according to Gilbert and Elliott (GE model) with the parameters | |||
:$$p_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 0.001, | :$$p_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 0.001,\hspace{0.2cm}p_{\rm B} = 0.1,\hspace{0.2cm}p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \\hspace{-0.1cm} 0.1, \hspace{0.2cm} p(\rm B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.01\hspace{0.05cm}.$$ | ||
\hspace{0.2cm}p_{\rm B} = 0.1, | |||
G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \ | |||
\hspace{-0.1cm} 0.1, \hspace{0.2cm} p(\rm | |||
B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.01\hspace{0.05cm}.$$ | |||
From these four probabilities, the corresponding characteristics of the channel model according to McCullough (MC model) can be determined in such a way that both models have exactly the same statistical properties, namely | |||
* | * exactly the same error distance distribution (EDD) $V_a(k)$, | ||
* | * exactly the same error correlation function (ECF) $\varphi_e(k)$. | ||
The probabilities of the MC model were determined in [[Aufgaben:Exercise_5.7:_McCullough_and_Gilbert-Elliott_Parameters|"Exercise 5.7"]] as follows $($labels according to the graph for Exercise 5.7, all with $q$ instead of $p)$: | |||
:$$q_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 0.0061, | :$$q_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 0.0061,\hspace{0.2cm}q_{\rm B} = 0.1949,\hspace{0.2cm}q(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \\hspace{-0.1cm} 0.5528, \hspace{0.2cm} q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.3724\hspace{0.05cm}.$$ | ||
\hspace{0.2cm}q_{\rm B} = 0.1949, | |||
G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \ | |||
\hspace{-0.1cm} 0.5528, \hspace{0.2cm} q(\rm | |||
B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.3724\hspace{0.05cm}.$$ | |||
The upper graph shows the functions $V_a(k)$ and $\varphi_e(k)$ simulatively determined from $N = 10^6$ sequence elements for the GE and MC models. There are still slight discrepancies here. In the limiting case for $N → ∞$, on the other hand, error correlation function and error distance distribution of both models agree exactly. | |||
In | In this exercise, important descriptive variables of the GE model such as | ||
*state probabilities, | |||
*mean error probabilities, and | |||
*correlation duration | |||
should be determined directly from the $q$ parameters of the MC model. | |||
=== | |||
''Notes:'' | |||
* The exercise belongs to the chapter [[Digital_Signal_Transmission/Burst_Error_Channels| "Burst Error Channels"]]. | |||
* From the above exercises, the following results can be further used: | |||
:(a) The state probabilities of the GE model are | |||
:$$w_{\rm G} = \frac{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B) + p(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)}\hspace{0.05cm},\hspace{0.2cm} w_{\rm B} = 1 - w_{\rm G}\hspace{0.05cm}.$$ | |||
:(b) The mean error probability of the GE model is | |||
:$$p_{\rm M} = w_{\rm G} \cdot p_{\rm G} + w_{\rm B} \cdot p_{\rm B}= \varphi_{e}(k = 0 )\hspace{0.05cm}.$$ | |||
:(c) The correlation duration of the GE model is calculated as | |||
:$$D_{\rm K} =\frac{1}{{\rm Pr}(\rm G\hspace{0.05cm}|\hspace{0.05cm}B ) + {\rm Pr}(\rm B\hspace{0.05cm}|\hspace{0.05cm} G )}-1\hspace{0.05cm}.$$ | |||
===Questions=== | |||
<quiz display=simple> | <quiz display=simple> | ||
{ | {Calculate the probabilities $\alpha_{\rm G}$ and $\alpha_{\rm B}$ that the MC model is in the state "Good" and the state "Bad". | ||
|type=" | |type="{}"} | ||
$\alpha_{\rm G} \hspace{0.05cm} = \ ${ 0.5975 3% } | |||
$\alpha_{\rm B} \ = \ ${ 0.4025 3% } | |||
{Determine the mean error distance of the MC model. | |||
|type="{}"} | |||
${\rm E}\big[a\big] \ = \ ${ 100.1 3% } | |||
{ | {What is the error correlation function value for $k = 0$? | ||
|type="{}"} | |type="{}"} | ||
$ | $\varphi_e(k = 0) \ = \ ${ 0.01 3% } | ||
{Give the error correlation duration $D_{\rm K}$ as a function of the MC parameters $q_{\rm G}, q_{\rm B}, q(\rm G\hspace{0.05cm}|\hspace{0.05cm}B)$ and $q(\rm B\hspace{0.05cm}|\hspace{0.05cm}G)$. <br>Which result is correct? | |||
|type="()"} | |||
- $D_{\rm K} = \big [q({\rm B\hspace{0.05cm}|\hspace{0.05cm}G}) + q({\rm G\hspace{0.05cm}|\hspace{0.05cm}B})\big]^{-1} \ -1$, | |||
+ $D_{\rm K} = \big [q_{\rm G} \cdot q({\rm G|B}) + q_{\rm B} \cdot q({\rm G|B}) \big]^{-1} \ -1$. | |||
</quiz> | </quiz> | ||
=== | ===Solution=== | ||
{{ML-Kopf}} | {{ML-Kopf}} | ||
'''(1)''' | '''(1)''' For the state probabilities of the GE model was determined in Exercise 5.6Z: | ||
'''(2)''' | :$$w_{\rm G} = \frac{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B) + p(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)} = 0.909\hspace{0.05cm},\hspace{0.5cm} w_{\rm B} = 1 - w_{\rm G}= 0.091\hspace{0.05cm}.$$ | ||
'''(3)''' | |||
'''(4)''' | *In contrast, for the MC model we obtain: | ||
'''( | :$$\alpha_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} \frac{q(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{q(\rm G\hspace{0.05cm}|\hspace{0.05cm} B) + q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)}= \frac{0.5528}{0.5528 +0.3724}\hspace{0.15cm}\underline {= 0.5975}\hspace{0.05cm},\hspace{0.5cm}\alpha_{\rm B} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 1 -\alpha_{\rm G} \hspace{0.15cm}\underline {= 0.4025}\hspace{0.05cm}.$$ | ||
*In subtask '''(3)''' of Exercise 5.7, these values have already been determined once, but from the parameters of the equivalent Gilbert-Elliott model. | |||
'''(2)''' The mean error distance in the channel state "GOOD" is equal to the reciprocal of the associated error probability $q_{\rm G}$. | |||
*Accordingly, the mean error distance in the state "BAD" is $1/q_{\rm B}$. | |||
*By weighting with the two state probabilities $\alpha_{\rm G}$ and $\alpha_{\rm B}$, the mean error distance of the MC model as a whole is given by | |||
:$${\rm E}[a] =\frac{\alpha_{\rm G}}{q_{\rm G}} + \frac{\alpha_{\rm B}}{q_{\rm B}}=\frac{0.5975}{0.0061} + \frac{0.4025}{0.1949} =97.95 + 2.06\hspace{0.15cm}\underline { = 100.1}\hspace{0.05cm}.$$ | |||
*Of course, this value should be exactly the same as for the corresponding GE model. | |||
*The small deviation of $0.1$ is due to rounding errors. | |||
'''(3)''' Again, the relation $\varphi_e(k = 0) = p_{\rm M}$ holds. | |||
*However, the mean error probability is equal to the reciprocal of the mean error distance ${\rm E}[a]$. | |||
*It follows that $\varphi_e(k = 0) \ \underline {= 0.01}$. | |||
'''(4)''' In the GE model, the correlation duration is given as follows ($S$ stands for sum): | |||
:$$D_{\rm K} = {1}/{S}-1 \hspace{0.05cm},\hspace{0.2cm}S = {\rm Pr}(\rm G\hspace{0.05cm}|\hspace{0.05cm} B ) + {\rm Pr}(\rm B\hspace{0.05cm}|\hspace{0.05cm} G )\hspace{0.05cm}.$$ | |||
*Further, using the data for Exercise 5.7: | |||
:$$q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G }) = \frac{\alpha_{\rm B} \cdot S}{\alpha_{\rm G} \cdot q_{\rm B} + \alpha_{\rm B} \cdotq_{\rm G}} \hspace{0.05cm}, \hspace{0.2cm}q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })= \frac{\alpha_{\rm G}}{\alpha_{\rm B}} \cdot q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)$$ | |||
:$$\Rightarrow \hspace{0.3cm} S = q_{\rm G} \cdot q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G }) + q_{\rm B} \cdot\frac{\alpha_{\rm G}}{\alpha_{\rm B}} \cdot q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G ) = q_{\rm G} \cdot q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G })+ q_{\rm B} \cdot q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B }) \hspace{0.05cm}.$$ | |||
:$$\Rightarrow \hspace{0.3cm}D_{\rm K} =\frac{1}{q_{\rm G} \cdotq({\rm B\hspace{0.05cm}|\hspace{0.05cm} G })+ q_{\rm B} \cdotq({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })}-1 \hspace{0.05cm}.$$ | |||
*So, the correct solution is <u>solution 2</u>. With the given parameter values, we obtain, for example: | |||
:$$D_{\rm K} =\frac{1}{0.0061 \cdot 0.3724 + 0.1949 \cdot0.5528}-1=\frac{1}{0.11}-1 {\approx 8.09}\hspace{0.05cm}.$$ | |||
*The result is exactly the same value as in subtask '''(3)''' of Exercise 5.6. | |||
{{ML-Fuß}} | {{ML-Fuß}} | ||
[[Category: | [[Category:Digital Signal Transmission: Exercises|^5.3 Burst Error Channels^]] | ||
[[de:Aufgaben:Aufgabe 5.7Z: Nochmals McCullough-Modell]] | |||
Latest revision as of 17:56, 16 March 2026

As in "Exercise 5.6", "Exercise 5.6Z" and "Exercise 5.7", we consider the burst error channel model according to Gilbert and Elliott (GE model) with the parameters
- $$p_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 0.001,\hspace{0.2cm}p_{\rm B} = 0.1,\hspace{0.2cm}p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \\hspace{-0.1cm} 0.1, \hspace{0.2cm} p(\rm B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.01\hspace{0.05cm}.$$
From these four probabilities, the corresponding characteristics of the channel model according to McCullough (MC model) can be determined in such a way that both models have exactly the same statistical properties, namely
- exactly the same error distance distribution (EDD) $V_a(k)$,
- exactly the same error correlation function (ECF) $\varphi_e(k)$.
The probabilities of the MC model were determined in "Exercise 5.7" as follows $($labels according to the graph for Exercise 5.7, all with $q$ instead of $p)$:
- $$q_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 0.0061,\hspace{0.2cm}q_{\rm B} = 0.1949,\hspace{0.2cm}q(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \\hspace{-0.1cm} 0.5528, \hspace{0.2cm} q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.3724\hspace{0.05cm}.$$
The upper graph shows the functions $V_a(k)$ and $\varphi_e(k)$ simulatively determined from $N = 10^6$ sequence elements for the GE and MC models. There are still slight discrepancies here. In the limiting case for $N → ∞$, on the other hand, error correlation function and error distance distribution of both models agree exactly.
In this exercise, important descriptive variables of the GE model such as
- state probabilities,
- mean error probabilities, and
- correlation duration
should be determined directly from the $q$ parameters of the MC model.
Notes:
- The exercise belongs to the chapter "Burst Error Channels".
- From the above exercises, the following results can be further used:
- (a) The state probabilities of the GE model are
- $$w_{\rm G} = \frac{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B) + p(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)}\hspace{0.05cm},\hspace{0.2cm} w_{\rm B} = 1 - w_{\rm G}\hspace{0.05cm}.$$
- (b) The mean error probability of the GE model is
- $$p_{\rm M} = w_{\rm G} \cdot p_{\rm G} + w_{\rm B} \cdot p_{\rm B}= \varphi_{e}(k = 0 )\hspace{0.05cm}.$$
- (c) The correlation duration of the GE model is calculated as
- $$D_{\rm K} =\frac{1}{{\rm Pr}(\rm G\hspace{0.05cm}|\hspace{0.05cm}B ) + {\rm Pr}(\rm B\hspace{0.05cm}|\hspace{0.05cm} G )}-1\hspace{0.05cm}.$$
Questions
Solution
- $$w_{\rm G} = \frac{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B) + p(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)} = 0.909\hspace{0.05cm},\hspace{0.5cm} w_{\rm B} = 1 - w_{\rm G}= 0.091\hspace{0.05cm}.$$
- In contrast, for the MC model we obtain:
- $$\alpha_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} \frac{q(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{q(\rm G\hspace{0.05cm}|\hspace{0.05cm} B) + q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)}= \frac{0.5528}{0.5528 +0.3724}\hspace{0.15cm}\underline {= 0.5975}\hspace{0.05cm},\hspace{0.5cm}\alpha_{\rm B} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 1 -\alpha_{\rm G} \hspace{0.15cm}\underline {= 0.4025}\hspace{0.05cm}.$$
- In subtask (3) of Exercise 5.7, these values have already been determined once, but from the parameters of the equivalent Gilbert-Elliott model.
(2) The mean error distance in the channel state "GOOD" is equal to the reciprocal of the associated error probability $q_{\rm G}$.
- Accordingly, the mean error distance in the state "BAD" is $1/q_{\rm B}$.
- By weighting with the two state probabilities $\alpha_{\rm G}$ and $\alpha_{\rm B}$, the mean error distance of the MC model as a whole is given by
- $${\rm E}[a] =\frac{\alpha_{\rm G}}{q_{\rm G}} + \frac{\alpha_{\rm B}}{q_{\rm B}}=\frac{0.5975}{0.0061} + \frac{0.4025}{0.1949} =97.95 + 2.06\hspace{0.15cm}\underline { = 100.1}\hspace{0.05cm}.$$
- Of course, this value should be exactly the same as for the corresponding GE model.
- The small deviation of $0.1$ is due to rounding errors.
(3) Again, the relation $\varphi_e(k = 0) = p_{\rm M}$ holds.
- However, the mean error probability is equal to the reciprocal of the mean error distance ${\rm E}[a]$.
- It follows that $\varphi_e(k = 0) \ \underline {= 0.01}$.
(4) In the GE model, the correlation duration is given as follows ($S$ stands for sum):
- $$D_{\rm K} = {1}/{S}-1 \hspace{0.05cm},\hspace{0.2cm}S = {\rm Pr}(\rm G\hspace{0.05cm}|\hspace{0.05cm} B ) + {\rm Pr}(\rm B\hspace{0.05cm}|\hspace{0.05cm} G )\hspace{0.05cm}.$$
- Further, using the data for Exercise 5.7:
- $$q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G }) = \frac{\alpha_{\rm B} \cdot S}{\alpha_{\rm G} \cdot q_{\rm B} + \alpha_{\rm B} \cdotq_{\rm G}} \hspace{0.05cm}, \hspace{0.2cm}q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })= \frac{\alpha_{\rm G}}{\alpha_{\rm B}} \cdot q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)$$
- $$\Rightarrow \hspace{0.3cm} S = q_{\rm G} \cdot q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G }) + q_{\rm B} \cdot\frac{\alpha_{\rm G}}{\alpha_{\rm B}} \cdot q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G ) = q_{\rm G} \cdot q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G })+ q_{\rm B} \cdot q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B }) \hspace{0.05cm}.$$
- $$\Rightarrow \hspace{0.3cm}D_{\rm K} =\frac{1}{q_{\rm G} \cdotq({\rm B\hspace{0.05cm}|\hspace{0.05cm} G })+ q_{\rm B} \cdotq({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })}-1 \hspace{0.05cm}.$$
- So, the correct solution is solution 2. With the given parameter values, we obtain, for example:
- $$D_{\rm K} =\frac{1}{0.0061 \cdot 0.3724 + 0.1949 \cdot0.5528}-1=\frac{1}{0.11}-1 {\approx 8.09}\hspace{0.05cm}.$$
- The result is exactly the same value as in subtask (3) of Exercise 5.6.