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

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* The binary symbol&nbsp; $\rm L$ &nbsp;&rArr;&nbsp; <i>Low</i>&nbsp; is always represented by the ternary symbol&nbsp; $\rm N$ &nbsp;&rArr;&nbsp; <i>Null</i>&nbsp;.
 
* The binary symbol&nbsp; $\rm L$ &nbsp;&rArr;&nbsp; <i>Low</i>&nbsp; is always represented by the ternary symbol&nbsp; $\rm N$ &nbsp;&rArr;&nbsp; <i>Null</i>&nbsp;.
* The binary symbol&nbsp; $\rm H$ &nbsp;&rArr;&nbsp; <i>High</i>&nbsp; is also coded deterministically but alternately (hence the name &bdquo;AMI&rdquo;) by the symbols&nbsp; $\rm P$ &nbsp;&rArr;&nbsp; <i>Plus</i>&nbsp; and&nbsp; $\rm M$ &nbsp;&rArr;&nbsp; <i>Minus</i>&nbsp; codiert.
+
* The binary symbol&nbsp; $\rm H$ &nbsp;&rArr;&nbsp; <i>High</i>&nbsp; is also coded deterministically but alternately (hence the name &bdquo;AMI&rdquo;) by the symbols&nbsp; $\rm P$ &nbsp;&rArr;&nbsp; <i>Plus</i>&nbsp; and&nbsp; $\rm M$ &nbsp;&rArr;&nbsp; <i>Minus</i>&nbsp;.
  
  
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'''(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) +  
 
\Rightarrow\hspace{0.3cm} H_1  = {1}/{4} \cdot {\rm log}_2\hspace{0.1cm} (4) +  
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'''(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/Tern\ddot{a}rsymbol}}  
 
  2 \cdot {1}/{8} \cdot {\rm log}_2\hspace{0.1cm}(8)  \hspace{0.15cm} \underline {= 1.06 \,{\rm bit/Tern\ddot{a}rsymbol}}  
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''Interpretation:''
 
''Interpretation:''
*Für&nbsp; $p_{\rm L} = 1/4, \ p_{\rm H} = 3/4$&nbsp; ergibt sich&nbsp; $H_1 = 1.56 \; \rm bit/Symbol$.  
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*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}
 
  \lim_{k \rightarrow \infty } H_k = 0.811 \,{\rm bit/Symbol}
 
  \hspace{0.05cm}.$$
 
  \hspace{0.05cm}.$$
Daraus folgt: <br>
+
It follows from this: <br>
*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;  
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*If one considers two message sources&nbsp; $\rm Q1$&nbsp; and&nbsp; $\rm Q2$&nbsp; with the same symbol range&nbsp; $M$ &nbsp; &#8658; &nbsp; decision content&nbsp; $H_0 = \rm const.$, whereby the first-order entropy approximation&nbsp; $\rm Q1$&nbsp; is clearly greater for source&nbsp; $(H_1)$&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;  
*Vielmehr muss man für beide Quellen
+
*Rather, one must
:* genügend viele Entropienäherungen&nbsp; $H_1$,&nbsp; $H_2$,&nbsp; $H_3$,&nbsp; ...  berechnen, und
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:* calculate enough entropy approximations&nbsp; $H_1$,&nbsp; $H_2$,&nbsp; $H_3$,&nbsp; ...  for both sources and
:* daraus (grafisch oder analytisch) den Grenzwert von&nbsp; $H_k$&nbsp; für&nbsp; $k \to \infty$&nbsp; bestimmen.
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:* determine from them (graphically or analytically) the limit value of&nbsp; $H_k$&nbsp; für&nbsp; $k \to \infty$&nbsp; .
  
*Erst dann ist eine endgültige Aussage über die Entropieverhältnisse möglich.
+
*Only then is a final statement about the entropy ratios possible.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  

Revision as of 18:24, 22 May 2021

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

We assume similar prerequisites as in  task 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 ties 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 code signal  $c(t)$  and the corresponding symbol 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  $\rm N$  ⇒  Null .
  • The binary symbol  $\rm H$  ⇒  High  is also coded deterministically but alternately (hence the name „AMI”) by the symbols  $\rm P$  ⇒  Plus  and  $\rm M$  ⇒  Minus .


In this task, the decision content  $H_0$  and the resulting entropy  $H_{\rm C}$  the code symbol 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  $H_{\rm C})$  und den Entropienäherungen:
$$H \le \ \text{...} \ \le H_3 \le H_2 \le H_1 \le H_0 \hspace{0.05cm}.$$
  • In  task 1.4  for equally probable symbols  $\rm L$  and  $\rm H$  the entropy approximations were calculated 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 code symbol sequence  $\langle c_\nu \rangle$?

$H_{\rm C} \ = \ $

$\ \rm bit/ternary symbol$

2

What is the relative redundancy of the code symbol 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 code symbol sequence?

$H_{\rm C} \ = \ $

$\ \rm bit/ternary symbol$

4

What is the relative redundancy of the code symbol sequence?

$r_{\rm C} \ = \ $

$\ \rm \%$

5

Berechnen Sie die Näherung  $H_{\rm 1}$  der Coderentropie für  $p_{\rm L} = 1/4$  und  $p_{\rm H} = 3/4$.

$H_{\rm 1} \ = \ $

$\ \rm bit/ternary symbol$

6

Calculate the approximation  $H_{\rm 1}$  der Coderentropie für  $p_{\rm L} = 3/4$  und  $p_{\rm H} = 1/4$.

$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 code symbol 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:
$$H_{\rm Q} {= 1 \,{\rm bit/Bin\ddot{a}rsymbol}} \hspace{0.3cm} \Rightarrow\hspace{0.3cm} H_{\rm C} \hspace{0.15cm} \underline {= 1 \,{\rm bit/Tern\ddot{a}rsymbol}} \hspace{0.05cm}.$$


(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:

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


(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 generalise 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:

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

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 range  $M$   ⇒   decision content  $H_0 = \rm const.$, whereby the first-order entropy approximation  $\rm Q1$  is clearly greater for source  $(H_1)$  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  $H_k$  für  $k \to \infty$  .
  • Only then is a final statement about the entropy ratios possible.