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Difference between revisions of "Aufgaben:Exercise 4.5Z: Again Mutual Information"

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{{quiz-Header|Buchseite=Informationstheorie/AWGN–Kanalkapazität bei wertkontinuierlichem Eingang
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{{quiz-Header|Buchseite=Information_Theory/AWGN_Channel_Capacity_for_Continuous_Input
 
}}
 
}}
  
[[File:P_ID2893__Inf_Z_4_5.png|right|]]
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[[File:EN_Inf_Z_4_5.png|right|frame|Given joint PDF and <br>graph of differential entropies]]
Die Grafik zeigt oben die in dieser Aufgabe zu betrachtende Verbund&ndash;WDF <i>f<sub>XY</sub></i>(<i>x</i>, <i>y</i>), die identisch ist mit der &bdquo;grünen&rdquo; Konstellation in
+
The graph above shows the joint PDF&nbsp; $f_{XY}(x, y)$&nbsp; to be considered in this task,&nbsp; which is identical to the "green" constellation in&nbsp; [[Aufgaben:Exercise_4.5:_Mutual_Information_from_2D-PDF|Exercise 4.5]].
[http://en.lntwww.de/Aufgaben:4.05_I(X;_Y)_aus_fXY(x,_y) '''Aufgabe A4.5.''']  Die Skizze ist in der <i>y</i>&ndash;Richtung um den Faktor 3 vergrößert. Im grün hinterlegten Definitionsgebiet ist die Verbund&ndash;WDF konstant gleich <i>C</i> = 1/<i>F</i>, wobei <i>F</i> die Fläche des Parallelogramms angibt.
+
* In this sketch&nbsp; $f_{XY}(x, y)&nbsp; is enlarged by a factor of&nbsp;3$&nbsp; in &nbsp; $y$&ndash;direction.
 +
*In the definition area highlighted in green, the joint PDF is constant equal to&nbsp; $C = 1/F$,&nbsp; where&nbsp; $F$&nbsp; indicates the area of the parallelogram.
  
In der Aufgabe A4.5 wurden folgende differentielle Entropien berechnet:
 
h(X) = log(A),
 
h(Y)=log(Be),
 
h(XY)=log(F)=log(AB).
 
In dieser Aufgabe sind nun die speziellen Parameterwerte <i>A</i> = e<sup>&ndash;2</sup> und <i>B</i> = e<sup>0.5</sup> zu verwenden. Außerdem ist zu beachten:
 
:* Bei Verwendung des <i>natürlichen Logarithmus</i> &bdquo;ln&rdquo; ist die Pseudo&ndash;Einheit &bdquo;nat&rdquo; anzufügen.
 
:* Verwendet man den <i>Logarithmus dualis</i> &#8658; &bdquo;log<sub>2</sub>&rdquo;, so ergeben sich alle  Größen in &bdquo;bit&rdquo;.
 
  
Entsprechend dem obigen Schaubild sollen nun auch die bedingten differentiellen Entropien <i>h</i>(<i>Y</i>|<i>X</i>) und <i>h</i>(<i>X</i>|<i>Y</i>) ermittelt und deren Bezug zur Transinformation <i>I</i>(<i>X</i>;<i>Y</i>) angegeben werden.
+
In Exercise 4.5 the following differential entropies were calculated:
 +
:$$h(X) \  =  \  {\rm log} \hspace{0.1cm} (\hspace{0.05cm}A\hspace{0.05cm})\hspace{0.05cm},$$
 +
:$$h(Y)   =    {\rm log} \hspace{0.1cm} (\hspace{0.05cm}B \cdot \sqrt{ {\rm e } } \hspace{0.05cm})\hspace{0.05cm},$$
 +
:$$h(XY)  =    {\rm log} \hspace{0.1cm} (\hspace{0.05cm}F \hspace{0.05cm}) = {\rm log} \hspace{0.1cm} (\hspace{0.05cm}A \cdot B \hspace{0.05cm})\hspace{0.05cm}.$$
 +
In this exercise, the parameter values&nbsp; A=e2&nbsp; and&nbsp; B=e0.5&nbsp; are now to be used.
  
'''Hinweis:''' Die Aufgabe gehört zum Themengebiet von [http://en.lntwww.de/Informationstheorie/AWGN–Kanalkapazität_bei_wertkontinuierlichem_Eingang '''Kapitel 4.2.''']
+
According to the above diagram, the conditional differential entropies&nbsp; h(Y|X)&nbsp;  and&nbsp; h(X|Y)&nbsp; should now also be determined and their relation to the mutual information&nbsp; I(X;Y)&nbsp; given.
  
  
  
===Fragebogen===
+
 
 +
 
 +
 
 +
 
 +
Hints:
 +
*The exercise belongs to the chapter&nbsp; [[Information_Theory/AWGN–Kanalkapazität_bei_wertkontinuierlichem_Eingang|AWGN channel capacity with continuous input]].
 +
*If the results are to be given in "nat", this is achieved with "log" &nbsp;&#8658;&nbsp; "ln".
 +
*If the results are to be given in "bit", this is achieved with "log" &nbsp;&#8658;&nbsp; "log<sub>2</sub>".
 +
 +
 
 +
 
 +
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
  
 
+
{State the following information theoretic quantities&nbsp; "nat":
{Geben Sie die folgenden informationstheoretischen Größen in &bdquo;nat&rdquo; an:
 
 
|type="{}"}
 
|type="{}"}
h(X) = { 2 3% }
+
$h(X) \ = \ $ { -2.06--1.94 }  nat
h(Y) = { 2 3% }
+
$h(Y) \ \hspace{0.03cm} = \ $ { 1 3% }  nat
h(XY) = { 1.5 3% }
+
$h(XY)\ \hspace{0.17cm} = \ $ { -1.55--1.45 }  nat
I(X;Y) = { 0.5 3% }
+
$I(X;Y)\ = \ $ { 0.5 3% }  nat
  
  
{Wie lauten die gleichen Größen mit der Pseudo&ndash;Einheit &bdquo;bit&rdquo;?
+
{What are the same quantities with the pseudo&ndash;unit&nbsp; "bit"?
 
|type="{}"}
 
|type="{}"}
h(X) = { 2.886 3% }
+
$h(X) \ = \ $ { -2.986--2.786  }  bit
h(Y) = { 1.443 3% }
+
$h(Y) \ \hspace{0.03cm} = \ $ { 1.443 3% }  bit
h(XY) = { 2.164 3% }
+
$h(XY)\ \hspace{0.17cm} = \ $ { -2.22--2.10 }  bit
I(X;Y) = { 0.721 3% }
+
$I(X;Y)\ = \ $ { 0.721 3% }  bit
  
  
{Berechnen Sie die bedingte differentielle Entropie <i>h</i>(<i>Y</i>|<i>X</i>).
+
{Calculate the conditional differential entropy&nbsp; $h(Y|X)$.
 
|type="{}"}
 
|type="{}"}
h(Y|X) = { 0.5 3% }
+
$h(Y|X) \ = \ $ { 0.5 3% }  nat
h(Y|X) = { 0.721 3% }
+
$h(Y|X) \ = \ $ { 0.721 3% }  bit
  
  
{Berechnen Sie die bedingte differentielle Entropie <i>h</i>(<i>X</i>|<i>Y</i>).
+
{Calculate the conditional differential entropy&nbsp; $h(X|Y)$.
 
|type="{}"}
 
|type="{}"}
h(X|Y) = { 2.5 3% }
+
$h(X|Y) \ = \ $ { -2.6--2.4 }  nat
h(X|Y) = { 3.607 3% }
+
$h(X|Y) \ = \ $ { -3.7--3.5 }  bit
  
  
{Welche der folgenden Größen sind niemals negativ?
+
{Which of the following quantities is never negative?
 
|type="[]"}
 
|type="[]"}
+ Sowohl <i>H</i>(<i>X</i>) als auch <i>H</i>(<i>Y</i>) im wertdiskreten Fall.
+
+ Both &nbsp;$H(X)&nbsp; and &nbsp;H(Y)$&nbsp; in the discrete case.
+ Die Transinformation <i>I</i>(<i>X</i>; <i>Y</i>) im wertdiskreten Fall.
+
+ The mutual information &nbsp;$I(X; Y)$&nbsp; in the discrete case.
+ Die Transinformation <i>I</i>(<i>X</i>; <i>Y</i>) im wertkontinuierlichen Fall.
+
+ The mutual information &nbsp;$I(X; Y)$&nbsp; in the continuous case.
- Sowohl <i>h</i>(<i>X</i>) als auch <i>h</i>(<i>Y</i>) im wertkontinuierlichen Fall.
+
- Both &nbsp;$h(X)&nbsp; and &nbsp;h(Y)$&nbsp;  in the continuous case.
- Sowohl <i>h</i>(<i>X</i>|<i>Y</i>) als auch <i>h</i>(<i>Y</i>|<i>X</i>) im wertkontinuierlichen Fall.
+
- Both &nbsp;$h(X|Y)&nbsp; and &nbsp;h(Y|X)$&nbsp; in the continuous case.
- Die Verbundentropie <i>h</i>(<i>XY</i>) im wertkontinuierlichen Fall.
+
- The joint entropy &nbsp;$h(XY)$&nbsp; in the continuous case.
 +
 
  
 
</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
<b>a)</b>&nbsp;&nbsp;Hier bietet sich die Verwendung des natürlichen Logarithmus an:
+
'''(1)'''&nbsp; Since the results are required in&nbsp; "nat",&nbsp; it is convenient to use the natural logarithm:
:*Die Zufallsgröße <i>X</i> ist gleichverteilt zwischen 0 und 1/e<sup>2</sup> = e<sup>&ndash;2</sup>:
+
*The random variable&nbsp; $X&nbsp; is uniformly distributed between&nbsp;0&nbsp; and&nbsp;1/{\rm e}^2={\rm e}^{-2}$:
$$h(X) =  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}{\rm e}^{-2}\hspace{0.05cm})
+
:$$h(X) =  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}{\rm e}^{-2}\hspace{0.05cm})
 
\hspace{0.15cm}\underline{= -2\,{\rm nat}}\hspace{0.05cm}. $$
 
\hspace{0.15cm}\underline{= -2\,{\rm nat}}\hspace{0.05cm}. $$
:*Die Zufallsgröße <i>Y</i> ist dreieckverteilt zwischen &plusmn;e<sup>0.5</sup>:
+
*The random variable&nbsp; $Y&nbsp; is triangularly distributed between&nbsp;&plusmn;{\rm e}^{-0.5}$:
$$h(Y) =  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}\sqrt{ {\rm e} } \cdot \sqrt{ {\rm e} } )
+
:$$h(Y) =  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}\sqrt{ {\rm e} } \cdot \sqrt{ {\rm e} } )
 
=  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}{ { \rm e } }  
 
=  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}{ { \rm e } }  
 
\hspace{0.05cm})
 
\hspace{0.05cm})
 
\hspace{0.15cm}\underline{= +1\,{\rm nat}}\hspace{0.05cm}.$$   
 
\hspace{0.15cm}\underline{= +1\,{\rm nat}}\hspace{0.05cm}.$$   
:* Die Fläche des Parallelogramms ergibt sich zu
+
* The area of the parallelogram is given by
F=AB=e2e0.5=e1.5.
+
:F=AB=e2e0.5=e1.5.
Damit hat die 2D&ndash;WDF im grün hinterlegten Bereich die konstante Höhe <i>C</i> = 1/<i>F</i> = e<sup>1.5</sup> und man erhält für die Verbundentropie:
+
*Thus, the 2D-PDF in the area highlighted in green has constant height&nbsp; $C = 1/F ={\rm e}^{1.5}$&nbsp; and we obtain for the joint entropy:
$$h(XY) =  {\rm ln} \hspace{0.1cm} (F)
+
:$$h(XY) =  {\rm ln} \hspace{0.1cm} (F)
 
=  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}{\rm e}^{-1.5}\hspace{0.05cm})
 
=  {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}{\rm e}^{-1.5}\hspace{0.05cm})
 
\hspace{0.15cm}\underline{= -1.5\,{\rm nat}}\hspace{0.05cm}.$$
 
\hspace{0.15cm}\underline{= -1.5\,{\rm nat}}\hspace{0.05cm}.$$
Daraus ergibt sich für die Transinformation:
+
*From this we obtain for the mutual information:
I(X;Y)=h(X)+h(Y)h(XY)=2nat+1nat(1.5nat)=0.5nat_.
+
:I(X;Y)=h(X)+h(Y)h(XY)=2nat+1nat(1.5nat)=0.5nat_.
<b>b)</b>&nbsp;&nbsp;Allgemein gilt der Zusammenhang log<sub>2</sub>(<i>x</i>) = ln(<i>x</i>)/ln(2).
+
 
h(X) = 2nat0.693nat/bit=2.886bit_,
+
 
h(Y) = +1nat0.693nat/bit=+1.443bit_,
+
 
h(XY) = 1.5nat0.693nat/bit=2.164bit_,
+
'''(2)'''&nbsp; In general, the relation&nbsp; $\log_2(x) = \ln(x)/\ln(2)$ holds.&nbsp; Thus, using the results of subtask&nbsp; '''(1)''', we obtain:
I(X;Y) = 0.5nat0.693nat/bit=0.721bit_.
+
:h(X) = 2nat0.693nat/bit=2.886bit_,
Oder auch:
+
:h(Y) = +1nat0.693nat/bit=+1.443bit_,
I(X;Y)=2.886bit+1.443bit+2.164bit=0.721bit.
+
:h(XY) = 1.5nat0.693nat/bit=2.164bit_,
<b>c)</b>&nbsp;&nbsp;Die Transinformation kann auch in der Form <i>I</i>(<i>X</i>; <i>Y</i>) = <i>h</i>(<i>Y</i>) &ndash; <i>h</i>(<i>Y</i>|<i>X</i>) geschrieben werden:
+
:I(X;Y) = 0.5nat0.693nat/bit=0.721bit_.
h(YX)=h(Y)I(X;Y)=1nat0.5nat=0.5nat=0.721bit_.
+
*Or also:
<b>d)</b>&nbsp;&nbsp;Für die differentielle Rückschlussentropie gilt entsprechend:
+
:I(X;Y)=2.886bit+1.443bit+2.164bit=0.721bit.
h(XY)=h(X)I(X;Y)=2nat0.5nat=2.5nat=3.607bit_.
+
 
Alle hier berechneten Größen sind in der Grafik am Seitenende zusammengestellt. Pfeile nach oben kennzeichnen einen positiven Beitrag, Pfeile nach unten einen negativen.
+
 
 +
 
 +
'''(3)'''&nbsp; The mutual information can also be written in the form &nbsp;$I(X;Y) = h(Y)-h(Y \hspace{-0.05cm}\mid \hspace{-0.05cm} X) $&nbsp;:
 +
:h(YX)=h(Y)I(X;Y)=1nat0.5nat=0.5nat=0.721bit_.
 +
 
 +
 
 +
 
 +
'''(4)'''&nbsp; For the differential inference entropy, it holds correspondingly:
 +
:h(XY)=h(X)I(X;Y)=2nat0.5nat=2.5nat=3.607bit_.
 +
 
 +
[[File: P_ID2898__Inf_Z_4_5d.png |right|frame|Summary of all results of this exercise]]
 +
*All quantities calculated here are summarized in the graph.&nbsp;
 +
*Arrows pointing up indicate a positive contribution, arrows pointing down indicate a negative contribution.
 +
 
 +
 
  
<b>e)</b>&nbsp;&nbsp;Richtig sind die <u>Lösungsvorschläge 1 bis 3</u>. Nochmals zur Verdeutlichung:
+
'''(5)'''&nbsp; Correct are the&nbsp; <u>proposed solutions 1 to 3</u>.
:* Für die Transinformation gilt stets <i>I</i>(<i>X</i>; <i>Y</i>) &#8805; 0.
+
:* Im wertdiskreten Fall gibt es keine negative Entropie, jedoch im wertkontinuierlichen.
+
Again for clarification:
[[File: P_ID2898__Inf_Z_4_5d.png |center|]]
+
* For the mutual information &nbsp;$I(X;Y) \ge 0$ always holds.
 +
* In the  discrete case there is no negative entropy, but in the  continuous case there is.
  
 
{{ML-Fuß}}
 
{{ML-Fuß}}
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[[Category:Aufgaben zu Informationstheorie|^4.2 AWGN–Kanalkapazität bei wertkontinuierlichem Eingang^]]
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[[Category:Information Theory: Exercises|^4.2 AWGN and Value-Continuous Input^]]

Latest revision as of 10:27, 11 October 2021

Given joint PDF and
graph of differential entropies

The graph above shows the joint PDF  fXY(x,y)  to be considered in this task,  which is identical to the "green" constellation in  Exercise 4.5.

  • In this sketch  fXY(x,y)  is enlarged by a factor of  3  in   y–direction.
  • In the definition area highlighted in green, the joint PDF is constant equal to  C=1/F,  where  F  indicates the area of the parallelogram.


In Exercise 4.5 the following differential entropies were calculated:

h(X) = log(A),
h(Y)=log(Be),
h(XY)=log(F)=log(AB).

In this exercise, the parameter values  A=e2  and  B=e0.5  are now to be used.

According to the above diagram, the conditional differential entropies  h(Y|X)  and  h(X|Y)  should now also be determined and their relation to the mutual information  I(X;Y)  given.




Hints:

  • The exercise belongs to the chapter  AWGN channel capacity with continuous input.
  • If the results are to be given in "nat", this is achieved with "log"  ⇒  "ln".
  • If the results are to be given in "bit", this is achieved with "log"  ⇒  "log2".



Questions

1

State the following information theoretic quantities  "nat":

h(X) = 

 nat
h(Y) = 

 nat
h(XY) = 

 nat
I(X;Y) = 

 nat

2

What are the same quantities with the pseudo–unit  "bit"?

h(X) = 

 bit
h(Y) = 

 bit
h(XY) = 

 bit
I(X;Y) = 

 bit

3

Calculate the conditional differential entropy  h(Y|X).

h(Y|X) = 

 nat
h(Y|X) = 

 bit

4

Calculate the conditional differential entropy  h(X|Y).

h(X|Y) = 

 nat
h(X|Y) = 

 bit

5

Which of the following quantities is never negative?

Both  H(X)  and  H(Y)  in the discrete case.
The mutual information  I(X;Y)  in the discrete case.
The mutual information  I(X;Y)  in the continuous case.
Both  h(X)  and  h(Y)  in the continuous case.
Both  h(X|Y)  and  h(Y|X)  in the continuous case.
The joint entropy  h(XY)  in the continuous case.


Solution

(1)  Since the results are required in  "nat",  it is convenient to use the natural logarithm:

  • The random variable  X  is uniformly distributed between  0  and  1/e2=e2:
h(X)=ln(e2)=2nat_.
  • The random variable  Y  is triangularly distributed between  ±e0.5:
h(Y)=ln(ee)=ln(e)=+1nat_.
  • The area of the parallelogram is given by
F=AB=e2e0.5=e1.5.
  • Thus, the 2D-PDF in the area highlighted in green has constant height  C=1/F=e1.5  and we obtain for the joint entropy:
h(XY)=ln(F)=ln(e1.5)=1.5nat_.
  • From this we obtain for the mutual information:
I(X;Y)=h(X)+h(Y)h(XY)=2nat+1nat(1.5nat)=0.5nat_.


(2)  In general, the relation  log2(x)=ln(x)/ln(2) holds.  Thus, using the results of subtask  (1), we obtain:

h(X) = 2nat0.693nat/bit=2.886bit_,
h(Y) = +1nat0.693nat/bit=+1.443bit_,
h(XY) = 1.5nat0.693nat/bit=2.164bit_,
I(X;Y) = 0.5nat0.693nat/bit=0.721bit_.
  • Or also:
I(X;Y)=2.886bit+1.443bit+2.164bit=0.721bit.


(3)  The mutual information can also be written in the form  I(X;Y)=h(Y)h(YX) :

h(YX)=h(Y)I(X;Y)=1nat0.5nat=0.5nat=0.721bit_.


(4)  For the differential inference entropy, it holds correspondingly:

h(XY)=h(X)I(X;Y)=2nat0.5nat=2.5nat=3.607bit_.
Summary of all results of this exercise
  • All quantities calculated here are summarized in the graph. 
  • Arrows pointing up indicate a positive contribution, arrows pointing down indicate a negative contribution.


(5)  Correct are the  proposed solutions 1 to 3.

Again for clarification:

  • For the mutual information  I(X;Y)0 always holds.
  • In the discrete case there is no negative entropy, but in the continuous case there is.