Difference between revisions of "Aufgaben:Exercise 5.7Z: McCullough Model once more"

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{{quiz-Header|Buchseite=Digitalsignalübertragung/Bündelfehlerkanäle}}
+
{{quiz-Header|Buchseite=Digital_Signal_Transmission/Burst_Error_Channels}}
  
[[File:P_ID1845__Dig_Z_5_7.png|right|frame|Fehlerabstandsverteilung und -korrelationsfunktion von GE–Modell und äquivalentem MC-Modell]]
+
[[File:P_ID1845__Dig_Z_5_7.png|right|frame|EDD and ECF of GE model and equivalent MC model]]
Wir betrachten wie auch in den Aufgaben A5.6, Z5.6 und A5.7 das Bündelfehler–Kanalmodell nach Gilbert und Elliott (GE–Modell) mit den Kenngrößen
+
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 B} = 0.1,\hspace{0.2cm}
:$$ p(\rm
+
p(\rm
 
G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \
 
G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \
 
\hspace{-0.1cm}  0.1, \hspace{0.2cm} p(\rm
 
\hspace{-0.1cm}  0.1, \hspace{0.2cm} p(\rm
 
B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.01\hspace{0.05cm}.$$
 
B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.01\hspace{0.05cm}.$$
  
Aus diesen vier Wahrscheinlichkeiten lassen sich die entsprechenden Kenngrößen des Kanalmodells nach McCullough (MC–Modell) so ermitteln, dass beide Modelle die genau gleichen statistischen Eigenschaften besitzen, nämlich
+
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
* exakt gleiche Fehlerabstandsverteilung $V_a(k)$,
+
* exactly the same error distance distribution (EDD)  $V_a(k)$,
* exakt gleiche Fehlerkorrelationsfunktion $\varphi_e(k)$.
+
* exactly the same error correlation function (ECF)  $\varphi_e(k)$.
  
  
Die Wahrscheinlichkeiten des MC–Modells wurden in der [[Aufgaben:5.7_MC-_aus_GE-Parameter| Aufgabe A5.7]] wie folgt ermittelt (Bezeichnungen entsprechend der Grafik zur Aufgabe A5.7, alle mit „$q$” anstelle von „$p$”):
+
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 B} = 0.1949,\hspace{0.2cm}
:$$ q(\rm
+
q(\rm
 
G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \
 
G\hspace{0.05cm}|\hspace{0.05cm} B)\hspace{-0.1cm} \ = \
 
\hspace{-0.1cm}  0.5528, \hspace{0.2cm} q(\rm
 
\hspace{-0.1cm}  0.5528, \hspace{0.2cm} q(\rm
 
B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.3724\hspace{0.05cm}.$$
 
B\hspace{0.05cm}|\hspace{0.05cm} G) = 0.3724\hspace{0.05cm}.$$
  
Die obere Grafik zeigt die aus $N = 10^6$ Folgenelementen simulativ ermittelten Funktionen $V_a(k)$ und $\varphi_e(k)$ für das GE– und das MC–Modell. Hier ergeben sich noch leichte Abweichungen. Im Grenzfall für $N → ∞$ stimmen dagegen Fehlerkorrelationsfunktion und Fehlerabstandsverteilung beider Modelle exakt überein.
+
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 dieser Aufgabe sollen nun wichtige Beschreibungsgrößen wie Zustandswahrscheinlichkeiten, mittlere Fehlerwahrscheinlichkeiten und Korrelationsdauer direkt aus den $q$–Parametern des MC–Modells ermittelt werden.
+
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.
  
''Hinweise:''
 
* Die Aufgabe gehört zum Kapitel [[Digitalsignal%C3%BCbertragung/B%C3%BCndelfehlerkan%C3%A4le| Bündelfehlerkanäle]].
 
* In der nachfolgenden [[5.7Z_Nochmals_MC-Modell|Aufgabe 5.7Z]] werden die wichtigsten Beschreibungsgrößen wie
 
** Fehlerkorrelationsfunktion,
 
** Korrelationsdauer,
 
** mittlere Fehlerwahrscheinlichkeit und
 
** Fehlerabstandsverteilung
 
  
: direkt aus den MC–Parametern berechnet.
 
* Sollte die Eingabe des Zahlenwertes „0” erforderlich sein, so geben Sie bitte „0.” ein.
 
  
''Hinweise:''
+
 
* Die Aufgabe gehört zum Themengebiet des Kapitels [[Digitalsignal%C3%BCbertragung/B%C3%BCndelfehlerkan%C3%A4le| Bündelfehlerkanäle]].
+
 
* Sollte die Eingabe des Zahlenwertes „0” erforderlich sein, so geben Sie bitte „0.” ein.
+
 
* Aus den oben genannten Aufgaben können folgende Ergebnisse weiterverwendet werden:
+
 
# - Die Zustandswahrscheinlichkeiten des GE–Modells sind
+
''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
 
:$$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
 
G\hspace{0.05cm}|\hspace{0.05cm} B) + p(\rm
Line 51: Line 50:
 
\hspace{0.05cm},\hspace{0.2cm} w_{\rm B} = 1 - w_{\rm G
 
\hspace{0.05cm},\hspace{0.2cm} w_{\rm B} = 1 - w_{\rm G
 
}\hspace{0.05cm}.$$
 
}\hspace{0.05cm}.$$
# - Die mittlere Fehlerwahrscheinlichkeit des GE–Modells beträgt
+
:(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}
 
:$$p_{\rm M} = w_{\rm G} \cdot p_{\rm G} + w_{\rm B} \cdot p_{\rm B}
 
= \varphi_{e}(k = 0 )\hspace{0.05cm}.$$
 
= \varphi_{e}(k = 0 )\hspace{0.05cm}.$$
# - Die Korrelationsdauer des GE–Modells berechnet sich zu
+
:(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}
 
:$$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
 
B ) + {\rm Pr}(\rm B\hspace{0.05cm}|\hspace{0.05cm} G )}-1
Line 61: Line 60:
  
  
===Fragebogen===
+
===Questions===
 
<quiz display=simple>
 
<quiz display=simple>
{Berechnen Sie die Wahrscheinlichkeiten $\alpha_G$ und $\alpha_B$, dass sich das MC&ndash;Modell im Zustand &bdquo;G&rdquo; bzw. im Zustand &bdquo;B&rdquo; befindet.
+
{Calculate the probabilities&nbsp; $\alpha_{\rm G}$&nbsp; and &nbsp;$\alpha_{\rm B}$ that the MC model is in the state "Good" and the state "Bad".
 
|type="{}"}
 
|type="{}"}
$\alpha_{\rm G} \ = \ ${ 0.5975 3% }
+
$\alpha_{\rm G} \hspace{0.05cm} = \ ${ 0.5975 3% }
 
$\alpha_{\rm B} \ = \ ${ 0.4025 3% }
 
$\alpha_{\rm B} \ = \ ${ 0.4025 3% }
  
{Ermitteln Sie den mittleren Fehlerabstand des MC&ndash;Modells.
+
{Determine the mean error distance of the MC model.
 
|type="{}"}
 
|type="{}"}
${\rm E}[a] \ = \ ${ 100.1 3% }  
+
${\rm E}\big[a\big] \ = \ ${ 100.1 3% }  
  
{Wie groß ist der FKF&ndash;Wert für $k = 0$?
+
{What is the error correlation function value for&nbsp; $k = 0$?
 
|type="{}"}
 
|type="{}"}
 
$\varphi_e(k = 0) \ = \ ${ 0.01 3% }  
 
$\varphi_e(k = 0) \ = \ ${ 0.01 3% }  
  
{Geben Sie die Fehlerkorrelationsdauer $D_{\rm K}$ als Funktion der MC&ndash;Parameter $q_{\rm G}, q_{\rm B}, q(\rm G|B)$ und $q(\rm B|G)$ an. Welches Ergebnis ist richtig?
+
{Give the error correlation duration&nbsp; $D_{\rm K}$&nbsp; as a function of the MC parameters&nbsp; $q_{\rm G},&nbsp; q_{\rm B},&nbsp; q(\rm G\hspace{0.05cm}|\hspace{0.05cm}B)$&nbsp; and&nbsp; $q(\rm B\hspace{0.05cm}|\hspace{0.05cm}G)$.&nbsp; <br>Which result is correct?
|type="[]"}
+
|type="()"}
- $D_{\rm K} = [q({\rm B|G}) + q({\rm G|B})]^{&ndash;1} \ &ndash;1$,
+
- $D_{\rm K} = \big  [q({\rm B\hspace{0.05cm}|\hspace{0.05cm}G}) + q({\rm G\hspace{0.05cm}|\hspace{0.05cm}B})\big]^{-1} \ -1$,
+ $D_{\rm K} = [q_{\rm G} \cdot q({\rm G|B}) + q_{\rm B} \cdot q({\rm G|B})]^{&ndash;1} \ &ndash;1$.
+
+ $D_{\rm K} = \big [q_{\rm G} \cdot q({\rm G|B}) + q_{\rm B} \cdot q({\rm G|B}) \big]^{-1} \ -1$.
 
</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Für die Zustandswahrscheinlichkeiten des GE&ndash;Modells wurde in Aufgabe Z5.6 ermittelt:
+
'''(1)'''&nbsp; For the state probabilities of the GE model was determined in Exercise 5.6Z:
 
:$$w_{\rm G} = \frac{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{p(\rm
 
:$$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
 
G\hspace{0.05cm}|\hspace{0.05cm} B) + p(\rm
 
B\hspace{0.05cm}|\hspace{0.05cm} G)} = 0.909
 
B\hspace{0.05cm}|\hspace{0.05cm} G)} = 0.909
\hspace{0.05cm},\hspace{0.2cm} w_{\rm B} = 1 - w_{\rm G
+
\hspace{0.05cm},\hspace{0.5cm} w_{\rm B} = 1 - w_{\rm G
 
}= 0.091\hspace{0.05cm}.$$
 
}= 0.091\hspace{0.05cm}.$$
  
Dagegen erhält man beim MC&ndash;Modell:
+
*In contrast, for the MC model we obtain:
 
:$$\alpha_{\rm G} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} \frac{q(\rm
 
:$$\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
 
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 +
 
B\hspace{0.05cm}|\hspace{0.05cm} G)}= \frac{0.5528}{0.5528 +
0.3724}\hspace{0.15cm}\underline {= 0.5975}\hspace{0.05cm},$$
+
0.3724}\hspace{0.15cm}\underline {= 0.5975}\hspace{0.05cm},\hspace{0.5cm}
:$$\alpha_{\rm B} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 1 -
+
\alpha_{\rm B} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 1 -
 
\alpha_{\rm G} \hspace{0.15cm}\underline {= 0.4025}\hspace{0.05cm}.$$
 
\alpha_{\rm G} \hspace{0.15cm}\underline {= 0.4025}\hspace{0.05cm}.$$
  
In der Teilaufgabe (3) der Aufgabe A5.7 wurden diese Werte schon einmal ermittelt, allerdings aus den Parametern des äquivalenten Gilbert&ndash;Elliott&ndash;Modells.
+
*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)'''&nbsp; Der mittlere Fehlerabstand im Kanalzustand &bdquo;GOOD&rdquo; ist gleich dem Kehrwert der dazugehörigen Fehlerwahrscheinlichkeit $q_{\rm G}$. Der mittlere Fehlerabstand im Zustand &bdquo;B&rdquo; ist dementsprechend $1/q_{\rm B}$. Durch Gewichtung mit den beiden Zustandswahrscheinlichkeiten $\alpha_{\rm G}$ und $\alpha_{\rm B}$ ergibt sich der mittlere Fehlerabstand des MC&ndash;Modells insgesamt zu
+
'''(2)'''&nbsp; 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
 
:$${\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} =
 
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}.$$
 
97.95 + 2.06\hspace{0.15cm}\underline { = 100.1}\hspace{0.05cm}.$$
  
Dieser Wert sollte natürlich genau so groß wie beim entsprechenden GE&ndash;Modell sein. Die kleine Abweichung von $0.1$ ist auf Rundungsfehler zurückzuführen.
+
*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)'''&nbsp; 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 $\varphi_e(k = 0) \ \underline {= 0.01}$.
  
'''(3)'''&nbsp; Auch hier gilt der Zusammenhang $\varphi_e(k = 0) = p_{\rm M}$. Die mittlere Fehlerwahrscheinlichkeit ist aber gleich dem Kehrwert des mittleren Fehlerabstands ${\rm E}[a]$. Daraus $\varphi_e(k = 0) \ \underline {= 0.01}$.
 
  
  
'''(4)'''&nbsp; Beim GE&ndash;Modell ist die Korrelationsdauer wie folgt gegeben ($S$ steht für Summe):
+
'''(4)'''&nbsp; 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
 
:$$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
 
Pr}(\rm G\hspace{0.05cm}|\hspace{0.05cm} B ) + {\rm Pr}(\rm
 
B\hspace{0.05cm}|\hspace{0.05cm} G )\hspace{0.05cm}.$$
 
B\hspace{0.05cm}|\hspace{0.05cm} G )\hspace{0.05cm}.$$
  
Weiter gilt mit den Angaben zur Aufgabe A5.7:
+
*Further, using the data for Exercise 5.7:
 
:$$q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G }) = \frac{\alpha_{\rm
 
:$$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} \cdot
 
B} \cdot S}{\alpha_{\rm G} \cdot q_{\rm B} + \alpha_{\rm B} \cdot
Line 136: Line 143:
 
q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })}-1 \hspace{0.05cm}.$$
 
q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })}-1 \hspace{0.05cm}.$$
  
Richtig ist also der <u>Lösungsvorschlag 2</u>. Mit den gegebenen Parameterwerten erhält man zum Beispiel:
+
*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 \cdot
 
:$$D_{\rm K} =\frac{1}{0.0061 \cdot 0.3724 + 0.1949 \cdot
 
0.5528}-1=\frac{1}{0.11}-1  {\approx 8.09}\hspace{0.05cm}.$$
 
0.5528}-1=\frac{1}{0.11}-1  {\approx 8.09}\hspace{0.05cm}.$$
  
Es ergibt sich exakt der gleiche Wert wie in der Aufgabe A5.6c.
+
*The result is exactly the same value as in subtask '''(3)''' of Exercise 5.6.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[Category:Aufgaben zu Digitalsignalübertragung|^5.3 Bündelfehlerkanäle^]]
+
[[Category:Digital Signal Transmission: Exercises|^5.3 Burst Error Channels^]]

Latest revision as of 09:22, 21 September 2022

EDD and ECF of GE model and equivalent MC model

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:

  • 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

1

Calculate the probabilities  $\alpha_{\rm G}$  and  $\alpha_{\rm B}$ that the MC model is in the state "Good" and the state "Bad".

$\alpha_{\rm G} \hspace{0.05cm} = \ $

$\alpha_{\rm B} \ = \ $

2

Determine the mean error distance of the MC model.

${\rm E}\big[a\big] \ = \ $

3

What is the error correlation function value for  $k = 0$?

$\varphi_e(k = 0) \ = \ $

4

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  $q(\rm B\hspace{0.05cm}|\hspace{0.05cm}G)$. 
Which result is correct?

$D_{\rm K} = \big [q({\rm B\hspace{0.05cm}|\hspace{0.05cm}G}) + q({\rm G\hspace{0.05cm}|\hspace{0.05cm}B})\big]^{-1} \ -1$,
$D_{\rm K} = \big [q_{\rm G} \cdot q({\rm G|B}) + q_{\rm B} \cdot q({\rm G|B}) \big]^{-1} \ -1$.


Solution

(1)  For the state probabilities of the GE model was determined in Exercise 5.6Z:

$$w_{\rm G} = \frac{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B)}{p(\rm G\hspace{0.05cm}|\hspace{0.05cm} B) + p(\rm B\hspace{0.05cm}|\hspace{0.05cm} G)} = 0.909 \hspace{0.05cm},\hspace{0.5cm} w_{\rm B} = 1 - w_{\rm G }= 0.091\hspace{0.05cm}.$$
  • 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:
$$q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G }) = \frac{\alpha_{\rm B} \cdot S}{\alpha_{\rm G} \cdot q_{\rm B} + \alpha_{\rm B} \cdot q_{\rm G}} \hspace{0.05cm}, \hspace{0.2cm}q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })= \frac{\alpha_{\rm G}}{\alpha_{\rm B}} \cdot q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G )$$
$$\Rightarrow \hspace{0.3cm} S = q_{\rm G} \cdot q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G }) + q_{\rm B} \cdot \frac{\alpha_{\rm G}}{\alpha_{\rm B}} \cdot q(\rm B\hspace{0.05cm}|\hspace{0.05cm} G ) = q_{\rm G} \cdot q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G })+ q_{\rm B} \cdot q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B }) \hspace{0.05cm}.$$
$$\Rightarrow \hspace{0.3cm}D_{\rm K} =\frac{1}{q_{\rm G} \cdot q({\rm B\hspace{0.05cm}|\hspace{0.05cm} G })+ q_{\rm B} \cdot q({\rm G\hspace{0.05cm}|\hspace{0.05cm} B })}-1 \hspace{0.05cm}.$$
  • So, the correct solution is solution 2. With the given parameter values, we obtain, for example:
$$D_{\rm K} =\frac{1}{0.0061 \cdot 0.3724 + 0.1949 \cdot 0.5528}-1=\frac{1}{0.11}-1 {\approx 8.09}\hspace{0.05cm}.$$
  • The result is exactly the same value as in subtask (3) of Exercise 5.6.