Aufgaben:Exercise 1.4Z: Entropy of the AMI Code: Difference between revisions

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{{quiz-Header|Buchseite=Informationstheorie/Nachrichtenquellen mit Gedächtnis
{{quiz-Header|Buchseite=Information_Theory/Discrete_Sources_with_Memory
}}
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[[File:P_ID2249__Inf_A_1_4.png|right|frame|Binäres Quellensignal (oben) und <br>ternäres Codersignal (unten)]]
[[File:P_ID2249__Inf_A_1_4.png|right|frame|Binary source signal (top) and <br>ternary encoder signal (bottom)]]
Wir gehen von ähnlichen Voraussetzungen wie in der&nbsp; [[Aufgaben:1.4_Entropienäherungen_für_den_AMI-Code|Aufgabe 1.4]]&nbsp; aus: &nbsp;  
We assume similar prerequisites as in&nbsp; [[Aufgaben:Exercise_1.4:_Entropy_Approximations_for_the_AMI_Code|Exercise 1.4]]&nbsp;: &nbsp;  


Eine Binärquelle liefert die Quellensybolfolge&nbsp; $\langle q_\nu \rangle$&nbsp;  mit&nbsp; $q_\nu \in \{ {\rm L}, {\rm H} \}$, wobei es keine statistischen Bindungen zwischen den einzelnen Folgenelementen gibt.
A binary source provides the source symbol sequence&nbsp; $\langle q_\nu \rangle$&nbsp;  with&nbsp; $q_\nu \in \{ {\rm L}, {\rm H} \}$, where there are no statistical bindings between the individual sequence elements.


Für die Symbolwahrscheinlichkeiten gelte:
For the symbol probabilities, let:
* $p_{\rm L} =p_{\rm H} = 1/2$&nbsp; (in den Teilaufgaben 1 und 2),
* $p_{\rm L} =p_{\rm H} = 1/2$&nbsp; (in subtasks 1 und 2),
* $p_{\rm L} = 1/4, \, p_{\rm H} = 3/4$&nbsp; (Teilaufgaben 3, 4 und 5),
* $p_{\rm L} = 1/4, \, p_{\rm H} = 3/4$&nbsp; (subtasks 3, 4 and 5),
* $p_{\rm L} = 3/4, \, p_{\rm H} = 1/4$&nbsp; (Teilaufgabe 6).
* $p_{\rm L} = 3/4, \, p_{\rm H} = 1/4$&nbsp; (subtask 6).




Das dargestellte Codersignal&nbsp; $c(t)$&nbsp; und die zugehörige Symbolfolge&nbsp; $\langle c_\nu \rangle$&nbsp;  mit&nbsp; $c_\nu \in \{{\rm P}, {\rm N}, {\rm M}  \}$&nbsp; ergibt sich aus der AMI&ndash;Codierung&nbsp; (<i>Alternate Mark Inversion</i>)&nbsp; nach folgender Vorschrift:
The presented coded signal&nbsp; $c(t)$&nbsp; and the corresponding encoded sequence&nbsp; $\langle c_\nu \rangle$&nbsp;  with&nbsp; $c_\nu \in \{{\rm P}, {\rm N}, {\rm M}  \}$&nbsp; results from the AMI coding&nbsp; ("Alternate Mark Inversion")&nbsp; according to the following rule:


* Das Binärsymbol&nbsp; $\rm L$ &nbsp;&rArr;&nbsp; <i>Low</i>&nbsp; wird stets durch das Ternärsymbol&nbsp; $\rm N$ &nbsp;&rArr;&nbsp; <i>Null</i>&nbsp; dargestellt.
* The binary symbol&nbsp; $\rm L$ &nbsp; &rArr; &nbsp; "Low"&nbsp; is always represented by the ternary symbol&nbsp; $\rm N$ &nbsp; &rArr; &nbsp; "German: Null"&nbsp; &rArr; &nbsp;"Zero".
* Das Binärsymbol&nbsp; $\rm H$ &nbsp;&rArr;&nbsp; <i>High</i>&nbsp; wird ebenfalls deterministisch, aber alternierend (daher der Name &bdquo;AMI&rdquo;) durch die Symbole&nbsp; $\rm P$ &nbsp;&rArr;&nbsp; <i>Plus</i>&nbsp; und&nbsp; $\rm M$ &nbsp;&rArr;&nbsp; <i>Minus</i>&nbsp; codiert.
* The binary symbol&nbsp; $\rm H$ &nbsp; &rArr; &nbsp; "High"&nbsp; is also encoded deterministically but alternately&nbsp; (hence the name "Alternate Mark Inversion")&nbsp; by the symbols&nbsp; $\rm P$ &nbsp;&rArr;&nbsp; "Plus"&nbsp; and&nbsp; $\rm M$ &nbsp;&rArr;&nbsp; "Minus".




In dieser Aufgabe sollen für die drei oben genannten Parametersätze der Entscheidungsgehalt&nbsp; $H_0$&nbsp; sowie die resultierende Entropie&nbsp; $H_{\rm C}$&nbsp; der Codesymbolfolge&nbsp; $\langle c_\nu \rangle$&nbsp; bestimmt werden.&nbsp; Die relative Redundanz der Codefolge ergibt sich daraus entsprechend der Gleichung
:$$r_{\rm C} = \frac{H_{\rm 0}-H_{\rm C}}{H_{\rm C}}
\hspace{0.05cm}.$$




In this task, the decision content&nbsp; $H_0$&nbsp; and the resulting entropy&nbsp; $H_{\rm C}$&nbsp; of the encoded sequence&nbsp; $\langle c_\nu \rangle$&nbsp; are to be determined for the three parameter sets mentioned above.&nbsp; The relative redundancy of the code sequence results from this according to the equation
:$$r_{\rm C} = \frac{H_{\rm 0}-H_{\rm C}}{H_{\rm C}}\hspace{0.05cm}.$$




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''Hinweise:''  
 
*Die Aufgabe gehört zum  Kapitel&nbsp; [[Informationstheorie/Nachrichtenquellen_mit_Gedächtnis|Nachrichtenquellen mit Gedächtnis]].
 
*Bezug genommen wird insbesondere auf die Seite&nbsp;  [[Informationstheorie/Nachrichtenquellen_mit_Gedächtnis#Die_Entropie_des_AMI.E2.80.93Codes|Die Entropie des AMI&ndash;Codes]].
''Hints:''  
*This task belongs to the chapter&nbsp; [[Information_Theory/Discrete_Sources_with_Memory|Discrete Sources with Memory]].
*Reference is made in particular to the page&nbsp;  [[Information_Theory/Discrete_Sources_with_Memory#The_entropy_of_the_AMI_code|The entropy of the AMI code]].
   
   
*Allgemein bestehen folgende Relationen zwischen dem Entscheidungsgehalt&nbsp; $H_0$,&nbsp; der Entropie&nbsp; $H$&nbsp; $($hier gleich&nbsp; $H_{\rm C})$&nbsp; und den Entropienäherungen:  
*In general, the following relations exist between the decision content&nbsp; $H_0$,&nbsp; the entropy&nbsp; $H$&nbsp; $($here equal to&nbsp; $H_{\rm C})$&nbsp; and the entropy approximations:  
:$$H \le \ \text{...} \  \le H_3 \le H_2 \le H_1 \le H_0  
:$$H \le \ \text{...} \  \le H_3 \le H_2 \le H_1 \le H_0\hspace{0.05cm}.$$
\hspace{0.05cm}.$$
*In&nbsp; [[Aufgaben:Exercise_1.4:_Entropy_Approximations_for_the_AMI_Code|Exercise 1.4]]&nbsp;&nbsp;  the entropy approximations were calculated for equally probable symbols&nbsp; $\rm L$&nbsp; and&nbsp; $\rm H$&nbsp; as follows (each in "bit/symbol"):  
*In&nbsp; [[Aufgaben:1.4_Entropienäherungen_für_den_AMI-Code|Aufgabe 1.4]]&nbsp; wurden für gleichwahrscheinliche Symbole&nbsp; $\rm L$&nbsp; und&nbsp; $\rm H$&nbsp; die Entropie&ndash;Näherungen wie folgt berechnet (jeweils in &bdquo;bit/Symbol&rdquo;):  
:$$H_1 = 1.500\hspace{0.05cm},\hspace{0.2cm} H_2 = 1.375\hspace{0.05cm},\hspace{0.2cm}H_3 = 1.292\hspace{0.05cm}.$$
:$$H_1 = 1.500\hspace{0.05cm},\hspace{0.2cm} H_2 = 1.375\hspace{0.05cm},\hspace{0.2cm}H_3 = 1.292
\hspace{0.05cm}.$$




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===Fragebogen===
===Questions===


<quiz display=simple>
<quiz display=simple>
{Die Quellensymbole seien gleichwahrscheinlich&nbsp; $(p_{\rm L} =  p_{\rm H}= 1/2)$.&nbsp; Wie groß ist die Entropie&nbsp; $H_{\rm C}$&nbsp; der Codesymbolfolge&nbsp; $\langle c_\nu \rangle$?
{Let the source symbols be equally probable&nbsp; $(p_{\rm L} =  p_{\rm H}= 1/2)$.&nbsp; What is the entropy&nbsp; $H_{\rm C}$&nbsp; of the encoded sequence&nbsp; $\langle c_\nu \rangle$?
|type="{}"}
|type="{}"}
$H_{\rm C} \ = \ $ { 1 3% } $\ \rm bit/Ternärsymbol$
$H_{\rm C} \ = \ $ { 1 3% } $\ \rm bit/ternary \ symbol$




{Wie groß ist die relative Redundanz der Codesymbolfolge?
{What is the relative redundancy of the encoded sequence?
|type="{}"}
|type="{}"}
$r_{\rm C} \ =  \ $ { 36.9 3% } $\ \rm \%$
$r_{\rm C} \ =  \ $ { 36.9 3% } $\ \rm \%$




{Für die Binärquelle gelte nun&nbsp; $p_{\rm L}  = 1/4$ &nbsp;und&nbsp; $p_{\rm H}  = 3/4$.&nbsp; Welcher Wert ergibt sich nun für die Entropie der Codesymbolfolge?
{For the binary source,&nbsp; $p_{\rm L}  = 1/4$ &nbsp;and&nbsp; $p_{\rm H}  = 3/4$.&nbsp; What is the entropy of the encoded sequence?
|type="{}"}
|type="{}"}
$H_{\rm C} \ = \ $ { 0.811 3% } $\ \rm bit/Ternärsymbol$
$H_{\rm C} \ = \ $ { 0.811 3% } $\ \rm bit/ternary \ symbol$
 


{What is the relative redundancy of the encoded sequence?


{Wie groß ist nun die relative Redundanz der Codesymbolfolge?
|type="{}"}
|type="{}"}
$r_{\rm C} \ = \ $ { 48.8 3% } $\ \rm \%$
$r_{\rm C} \ = \ $ { 48.8 3% } $\ \rm \%$




{Berechnen Sie die Näherung&nbsp; $H_{\rm 1}$&nbsp; der Coderentropie für&nbsp; $p_{\rm L} = 1/4$ &nbsp;und&nbsp; $p_{\rm H} = 3/4$.
{Calculate the approximation&nbsp; $H_{\rm 1}$&nbsp; of the code entropy for&nbsp; $p_{\rm L} = 1/4$ &nbsp;and&nbsp; $p_{\rm H} = 3/4$.
|type="{}"}
|type="{}"}
$H_{\rm 1} \ = \ $ { 1.56 3% } $\ \rm bit/Ternärsymbol$
$H_{\rm 1} \ = \ $ { 1.56 3% } $\ \rm bit/ternary \ symbol$




{Berechnen Sie die Näherung&nbsp; $H_{\rm 1}$&nbsp; der Coderentropie für&nbsp; $p_{\rm L} = 3/4$ &nbsp;und&nbsp; $p_{\rm H} = 1/4$.
{Calculate the approximation&nbsp; $H_{\rm 1}$&nbsp; of the code entropy for&nbsp; $p_{\rm L} = 3/4$ &nbsp;and&nbsp; $p_{\rm H} = 1/4$.
|type="{}"}
|type="{}"}
$H_{\rm 1} \ = \ $ { 1.06 3% } $\ \rm bit/Ternärsymbol$
$H_{\rm 1} \ = \ $ { 1.06 3% } $\ \rm bit/ternary \ symbol$




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</quiz>
</quiz>


===Musterlösung===
===Solution===
{{ML-Kopf}}
{{ML-Kopf}}
'''(1)'''&nbsp; Da durch den AMI&ndash;Code weder neue Information hinzukommt noch Information verschwindet, ist die Entropie&nbsp; $H_{\rm C}$&nbsp; der Codesymbolfolge&nbsp; $\langle c_\nu \rangle$&nbsp; gleich der Quellenentropie&nbsp; $H_{\rm Q}$.&nbsp;  
'''(1)'''&nbsp; Since the AMI code neither adds new information nor causes information to disappear, the entropy&nbsp; $H_{\rm C}$&nbsp; of the encoded sequence&nbsp; $\langle c_\nu \rangle$&nbsp; is equal to the source entropy&nbsp; $H_{\rm Q}$.&nbsp;  
*Bei gleichwahrscheinlichen und statistisch voneinander unabhängigen Quellensymbolen gilt deshalb:
*Therefore, for equally probable and statistically independent source symbols, the following holds:
:$$H_{\rm Q}    {= 1 \,{\rm bit/Bin\ddot{a}rsymbol}} \hspace{0.3cm} \Rightarrow\hspace{0.3cm} H_{\rm C}    \hspace{0.15cm} \underline {= 1 \,{\rm bit/Tern\ddot{a}rsymbol}}  
:$$H_{\rm Q}    {= 1 \,{\rm bit/binary \ symbol}} \hspace{0.3cm} \Rightarrow\hspace{0.3cm} H_{\rm C}    \hspace{0.15cm} \underline {= 1 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
\hspace{0.05cm}.$$






'''(2)'''&nbsp; Der Entscheidungsgehalt einer ternären Quelle beträgt&nbsp; $H_0 = \log_2 \; (3) = 1.585\; \rm bit/Symbol$.&nbsp;
'''(2)'''&nbsp; The decision content of a ternary source is&nbsp; $H_0 = \log_2 \; (3) = 1.585\; \rm bit/symbol$.&nbsp;
* Damit ergibt sich für die relative Redundanz
* This gives the following for the relative redundancy
:$$r_{\rm C} =1 -{H_{\rm C}/H_{\rm 0}}=1-1/{\rm log}_2\hspace{0.05cm}(3)  
:$$r_{\rm C} =1 -{H_{\rm C}/H_{\rm 0}}=1-1/{\rm log}_2\hspace{0.05cm}(3)\hspace{0.15cm} \underline {= 36.9  \,\%}\hspace{0.05cm}.$$
\hspace{0.15cm} \underline {= 36.9  \,\%}
\hspace{0.05cm}.$$






'''(3)'''&nbsp; Es gilt weiter&nbsp; $H_{\rm C} = H_{\rm Q}$.&nbsp; Wegen den ungleichen Symbolwahrscheinlichkeiten ist aber nun&nbsp; $H_{\rm Q}$&nbsp; kleiner:
'''(3)'''&nbsp; &nbsp; $H_{\rm C} = H_{\rm Q}$ is still valid.&nbsp; However, because of the unequal symbol probabilities,&nbsp; $H_{\rm Q}$&nbsp; is now smaller:
:$$H_{\rm Q}  =  \frac{1}{4} \cdot {\rm log}_2\hspace{0.05cm} (4) + \frac{3}{4} \cdot  
:$$H_{\rm Q}  =  \frac{1}{4} \cdot {\rm log}_2\hspace{0.05cm} (4) + \frac{3}{4} \cdot{\rm log}_2\hspace{0.1cm} (4/3){= 0.811 \,{\rm bit/binary \ symbol}} \hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_{\rm C}  = H_{\rm Q}  \hspace{0.15cm} \underline {= 0.811 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
{\rm log}_2\hspace{0.1cm} (4/3)
{= 0.811 \,{\rm bit/Bin\ddot{a}rsymbol}} \hspace{0.3cm}
\Rightarrow\hspace{0.3cm} H_{\rm C}  = H_{\rm Q}  \hspace{0.15cm} \underline {= 0.811 \,{\rm bit/Tern\ddot{a}rsymbol}}
\hspace{0.05cm}.$$






'''(4)'''&nbsp; In Analogie zur Teilaufgabe&nbsp; '''(2)'''&nbsp; gilt nun&nbsp; $r_{\rm C} = 1 -  0.811/1.585
'''(4)'''&nbsp; By analogy with sub-task&nbsp; '''(2)'''&nbsp; &nbsp; $r_{\rm C} = 1 -  0.811/1.585
  \hspace{0.15cm} \underline {= 48.8  \,\%}
  \hspace{0.15cm} \underline {= 48.8  \,\%}
  \hspace{0.05cm}.$
  \hspace{0.05cm}$ now holds.
*Man kann dieses Ergebnis verallgemeinern. Es gilt nämlich:
*One can generalize this result.&nbsp; Namely, it holds:
:$$(1-0.488) = (1- 0.189) \cdot (1- 0.369)\hspace{0.3cm}
:$$(1-0.488) = (1- 0.189) \cdot (1- 0.369)\hspace{0.3cm}\Rightarrow\hspace{0.3cm} (1-r_{\rm Codefolge})  = (1-r_{\rm Quelle}) \cdot (1- r_{\rm AMI-Code})\hspace{0.05cm}.$$
\Rightarrow\hspace{0.3cm} (1-r_{\rm Codefolge})  = (1-r_{\rm Quelle}) \cdot (1- r_{\rm AMI-Code})
\hspace{0.05cm}.$$






'''(5)'''&nbsp; Da jedes&nbsp; $\rm L$&nbsp; auf&nbsp; $\rm N$&nbsp; abgebildet wird und&nbsp; $\rm H$&nbsp; alternierend auf&nbsp; $\rm M$&nbsp; und&nbsp; $\rm P$, gilt
'''(5)'''&nbsp; Since each&nbsp; $\rm L$&nbsp; is mapped to&nbsp; $\rm N$&nbsp; and&nbsp; $\rm H$&nbsp; is mapped alternately to&nbsp; $\rm M$&nbsp; and&nbsp; $\rm P$, it holds that
:$$p_{\rm N} = p_{\rm L} = 1/4\hspace{0.05cm},\hspace{0.2cm}p_{\rm P} = p_{\rm M} = p_{\rm H}/2 = 3/8\hspace{0.3cm}  
:$$p_{\rm N} = p_{\rm L} = 1/4\hspace{0.05cm},\hspace{0.2cm}p_{\rm P} = p_{\rm M} = p_{\rm H}/2 = 3/8\hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_1  = {1}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4) +2 \cdot {3}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8/3)  \hspace{0.15cm} \underline {= 1.56 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
\Rightarrow\hspace{0.3cm} H_1  = {1}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4) +  
2 \cdot {3}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8/3)  \hspace{0.15cm} \underline {= 1.56 \,{\rm bit/Tern\ddot{a}rsymbol}}
\hspace{0.05cm}.$$




'''(6)'''&nbsp; Nun ergeben sich die Auftrittswahrscheinlichkeiten der Ternärsymbole zu&nbsp;  $p_{\rm N} = 3/4$&nbsp; sowie&nbsp; $p_{\rm P} = p_{\rm M} =1/8$.&nbsp; Somit gilt:
'''(6)'''&nbsp; Now the probabilities of occurrence of the ternary symbols are &nbsp;  $p_{\rm N} = 3/4$&nbsp; sowie&nbsp; $p_{\rm P} = p_{\rm M} =1/8$.&nbsp; Thus:
:$$H_1  = {3}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4/3) +  
:$$H_1  = {3}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4/3) +2 \cdot {1}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8)  \hspace{0.15cm} \underline {= 1.06 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$
2 \cdot {1}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8)  \hspace{0.15cm} \underline {= 1.06 \,{\rm bit/Tern\ddot{a}rsymbol}}  
\hspace{0.05cm}.$$


''Interpretation:''
''Interpretation:''
*Für&nbsp; $p_{\rm L} = 1/4, \ p_{\rm H} = 3/4$&nbsp; ergibt sich&nbsp; $H_1 = 1.56 \; \rm bit/Symbol$.  
*For&nbsp; $p_{\rm L} = 1/4, \ p_{\rm H} = 3/4$&nbsp; gives&nbsp; $H_1 = 1.56 \; \rm bit/symbol$.  
*Für&nbsp; $p_{\rm L} = 3/4, \ p_{\rm H} = 1/4$&nbsp; ergibt sich dagegen mit&nbsp; $H_1 = 1.06 \; \rm bit/Symbol$&nbsp; ein deutlich kleinerer Wert.
*For&nbsp; $p_{\rm L} = 3/4, \ p_{\rm H} = 1/4$&nbsp;, on the other hand,&nbsp; $H_1 = 1.06 \; \rm bit/symbol$&nbsp; results in a significantly smaller value.
*Für beide Parameterkombinationen gilt aber gleichermaßen:
*For both parameter combinations, however, the same applies:
:$$H_0  = 1.585 \,{\rm bit/Symbol}\hspace{0.05cm},\hspace{0.2cm}H_{\rm C} =  
:$$H_0  = 1.585 \,{\rm bit/symbol}\hspace{0.05cm},\hspace{0.2cm}H_{\rm C} =\lim_{k \rightarrow \infty } H_k = 0.811 \,{\rm bit/symbol}\hspace{0.05cm}.$$
\lim_{k \rightarrow \infty } H_k = 0.811 \,{\rm bit/Symbol}
It follows from this: <br>
\hspace{0.05cm}.$$
*If one considers two message sources&nbsp; $\rm Q1$&nbsp; and&nbsp; $\rm Q2$&nbsp; with the same symbol set size&nbsp; $M$ &nbsp; &#8658; &nbsp; decision content&nbsp; $H_0 = \rm const.$, whereby the first order entropy approximation&nbsp; $(H_1)$&nbsp; is clearly greater for source&nbsp; $\rm Q1$&nbsp; than for source&nbsp; $\rm Q2$, one cannot conclude from this by any means that the entropy of&nbsp; $\rm Q1$&nbsp; is actually greater than the entropy of $\rm Q2$.&nbsp;  
Daraus folgt: <br>
*Rather, one must
*Betrachtet man zwei Nachrichtenquellen&nbsp; $\rm Q1$&nbsp; und&nbsp; $\rm Q2$&nbsp; mit gleichem Symbolumfang&nbsp; $M$ &nbsp; &#8658; &nbsp; Entscheidungsgehalt&nbsp; $H_0 = \rm const.$, wobei bei der Quelle&nbsp; $\rm Q1$&nbsp; die Entropienäherung erster Ordnung&nbsp; $(H_1)$&nbsp; deutlich größer ist als bei der Quelle&nbsp; $\rm Q2$, so kann man daraus noch lange nicht schließen, dass die Entropie von&nbsp; $\rm Q1$&nbsp; tatsächlich größer ist als die Entropie von $\rm Q2$.&nbsp;  
:* calculate enough entropy approximations&nbsp; $H_1$,&nbsp; $H_2$,&nbsp; $H_3$,&nbsp; ...  for both sources and
*Vielmehr muss man für beide Quellen
:* determine from them&nbsp; (graphically or analytically)&nbsp; the limit value of&nbsp; $H_k$&nbsp; for&nbsp; $k \to \infty$.
:* genügend viele Entropienäherungen&nbsp; $H_1$,&nbsp; $H_2$,&nbsp; $H_3$,&nbsp; ...  berechnen, und
:* daraus (grafisch oder analytisch) den Grenzwert von&nbsp; $H_k$&nbsp; für&nbsp; $k \to \infty$&nbsp; bestimmen.


*Erst dann ist eine endgültige Aussage über die Entropieverhältnisse möglich.
*Only then a final statement about the entropy ratios is possible.
{{ML-Fuß}}
{{ML-Fuß}}






[[Category:Aufgaben zu Informationstheorie|^1.2 Nachrichtenquellen mit Gedächtnis^]]
[[Category:Information Theory: Exercises|^1.2 Sources with Memory^]]
[[de:Aufgaben:Aufgabe 1.4Z: Entropie der AMI-Codierung]]

Latest revision as of 17:57, 16 March 2026

Binary source signal (top) and
ternary encoder signal (bottom)

We assume similar prerequisites as in  Exercise 1.4 :  

A binary source provides the source symbol sequence  $\langle q_\nu \rangle$  with  $q_\nu \in \{ {\rm L}, {\rm H} \}$, where there are no statistical bindings between the individual sequence elements.

For the symbol probabilities, let:

  • $p_{\rm L} =p_{\rm H} = 1/2$  (in subtasks 1 und 2),
  • $p_{\rm L} = 1/4, \, p_{\rm H} = 3/4$  (subtasks 3, 4 and 5),
  • $p_{\rm L} = 3/4, \, p_{\rm H} = 1/4$  (subtask 6).


The presented coded signal  $c(t)$  and the corresponding encoded sequence  $\langle c_\nu \rangle$  with  $c_\nu \in \{{\rm P}, {\rm N}, {\rm M} \}$  results from the AMI coding  ("Alternate Mark Inversion")  according to the following rule:

  • The binary symbol  $\rm L$   ⇒   "Low"  is always represented by the ternary symbol  $\rm N$   ⇒   "German: Null"  ⇒  "Zero".
  • The binary symbol  $\rm H$   ⇒   "High"  is also encoded deterministically but alternately  (hence the name "Alternate Mark Inversion")  by the symbols  $\rm P$  ⇒  "Plus"  and  $\rm M$  ⇒  "Minus".



In this task, the decision content  $H_0$  and the resulting entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  are to be determined for the three parameter sets mentioned above.  The relative redundancy of the code sequence results from this according to the equation

$$r_{\rm C} = \frac{H_{\rm 0}-H_{\rm C}}{H_{\rm C}}\hspace{0.05cm}.$$




Hints:

  • In general, the following relations exist between the decision content  $H_0$,  the entropy  $H$  $($here equal to  $H_{\rm C})$  and the entropy approximations:
$$H \le \ \text{...} \ \le H_3 \le H_2 \le H_1 \le H_0\hspace{0.05cm}.$$
  • In  Exercise 1.4   the entropy approximations were calculated for equally probable symbols  $\rm L$  and  $\rm H$  as follows (each in "bit/symbol"):
$$H_1 = 1.500\hspace{0.05cm},\hspace{0.2cm} H_2 = 1.375\hspace{0.05cm},\hspace{0.2cm}H_3 = 1.292\hspace{0.05cm}.$$




Questions

1 Let the source symbols be equally probable  $(p_{\rm L} = p_{\rm H}= 1/2)$.  What is the entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$?

$H_{\rm C} \ = \ $ $\ \rm bit/ternary \ symbol$

2 What is the relative redundancy of the encoded sequence?

$r_{\rm C} \ = \ $ $\ \rm \%$

3 For the binary source,  $p_{\rm L} = 1/4$  and  $p_{\rm H} = 3/4$.  What is the entropy of the encoded sequence?

$H_{\rm C} \ = \ $ $\ \rm bit/ternary \ symbol$

4 What is the relative redundancy of the encoded sequence?

$r_{\rm C} \ = \ $ $\ \rm \%$

5 Calculate the approximation  $H_{\rm 1}$  of the code entropy for  $p_{\rm L} = 1/4$  and  $p_{\rm H} = 3/4$.

$H_{\rm 1} \ = \ $ $\ \rm bit/ternary \ symbol$

6 Calculate the approximation  $H_{\rm 1}$  of the code entropy for  $p_{\rm L} = 3/4$  and  $p_{\rm H} = 1/4$.

$H_{\rm 1} \ = \ $ $\ \rm bit/ternary \ symbol$


Solution

(1)  Since the AMI code neither adds new information nor causes information to disappear, the entropy  $H_{\rm C}$  of the encoded sequence  $\langle c_\nu \rangle$  is equal to the source entropy  $H_{\rm Q}$. 

  • Therefore, for equally probable and statistically independent source symbols, the following holds:
$$H_{\rm Q} {= 1 \,{\rm bit/binary \ symbol}} \hspace{0.3cm} \Rightarrow\hspace{0.3cm} H_{\rm C} \hspace{0.15cm} \underline {= 1 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$


(2)  The decision content of a ternary source is  $H_0 = \log_2 \; (3) = 1.585\; \rm bit/symbol$. 

  • This gives the following for the relative redundancy
$$r_{\rm C} =1 -{H_{\rm C}/H_{\rm 0}}=1-1/{\rm log}_2\hspace{0.05cm}(3)\hspace{0.15cm} \underline {= 36.9 \,\%}\hspace{0.05cm}.$$


(3)    $H_{\rm C} = H_{\rm Q}$ is still valid.  However, because of the unequal symbol probabilities,  $H_{\rm Q}$  is now smaller:

$$H_{\rm Q} = \frac{1}{4} \cdot {\rm log}_2\hspace{0.05cm} (4) + \frac{3}{4} \cdot{\rm log}_2\hspace{0.1cm} (4/3){= 0.811 \,{\rm bit/binary \ symbol}} \hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_{\rm C} = H_{\rm Q} \hspace{0.15cm} \underline {= 0.811 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$


(4)  By analogy with sub-task  (2)    $r_{\rm C} = 1 - 0.811/1.585

\hspace{0.15cm} \underline {= 48.8  \,\%}
\hspace{0.05cm}$ now holds.
  • One can generalize this result.  Namely, it holds:
$$(1-0.488) = (1- 0.189) \cdot (1- 0.369)\hspace{0.3cm}\Rightarrow\hspace{0.3cm} (1-r_{\rm Codefolge}) = (1-r_{\rm Quelle}) \cdot (1- r_{\rm AMI-Code})\hspace{0.05cm}.$$


(5)  Since each  $\rm L$  is mapped to  $\rm N$  and  $\rm H$  is mapped alternately to  $\rm M$  and  $\rm P$, it holds that

$$p_{\rm N} = p_{\rm L} = 1/4\hspace{0.05cm},\hspace{0.2cm}p_{\rm P} = p_{\rm M} = p_{\rm H}/2 = 3/8\hspace{0.3cm}\Rightarrow\hspace{0.3cm} H_1 = {1}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4) +2 \cdot {3}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8/3) \hspace{0.15cm} \underline {= 1.56 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$


(6)  Now the probabilities of occurrence of the ternary symbols are   $p_{\rm N} = 3/4$  sowie  $p_{\rm P} = p_{\rm M} =1/8$.  Thus:

$$H_1 = {3}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4/3) +2 \cdot {1}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8) \hspace{0.15cm} \underline {= 1.06 \,{\rm bit/ternary \ symbol}}\hspace{0.05cm}.$$

Interpretation:

  • For  $p_{\rm L} = 1/4, \ p_{\rm H} = 3/4$  gives  $H_1 = 1.56 \; \rm bit/symbol$.
  • For  $p_{\rm L} = 3/4, \ p_{\rm H} = 1/4$ , on the other hand,  $H_1 = 1.06 \; \rm bit/symbol$  results in a significantly smaller value.
  • For both parameter combinations, however, the same applies:
$$H_0 = 1.585 \,{\rm bit/symbol}\hspace{0.05cm},\hspace{0.2cm}H_{\rm C} =\lim_{k \rightarrow \infty } H_k = 0.811 \,{\rm bit/symbol}\hspace{0.05cm}.$$

It follows from this:

  • If one considers two message sources  $\rm Q1$  and  $\rm Q2$  with the same symbol set size  $M$   ⇒   decision content  $H_0 = \rm const.$, whereby the first order entropy approximation  $(H_1)$  is clearly greater for source  $\rm Q1$  than for source  $\rm Q2$, one cannot conclude from this by any means that the entropy of  $\rm Q1$  is actually greater than the entropy of $\rm Q2$. 
  • Rather, one must
  • calculate enough entropy approximations  $H_1$,  $H_2$,  $H_3$,  ... for both sources and
  • determine from them  (graphically or analytically)  the limit value of  $H_k$  for  $k \to \infty$.
  • Only then a final statement about the entropy ratios is possible.