Difference between revisions of "Aufgaben:Exercise 4.5Z: Again Mutual Information"
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− | {{quiz-Header|Buchseite= | + | {{quiz-Header|Buchseite=Information_Theory/AWGN_Channel_Capacity_for_Continuous_Input |
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− | [[File: | + | [[File:EN_Inf_Z_4_5.png|right|frame|Given joint PDF and <br>graph of differential entropies]] |
− | + | The graph above shows the joint PDF $f_{XY}(x, y)$ to be considered in this task, which is identical to the "green" constellation in [[Aufgaben:Exercise_4.5:_Mutual_Information_from_2D-PDF|Exercise 4.5]]. | |
− | [ | + | * In this sketch $f_{XY}(x, y) is enlarged by a factor of 3$ 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. | ||
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− | + | 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 A=e−2 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 [[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" ⇒ "ln". | ||
+ | *If the results are to be given in "bit", this is achieved with "log" ⇒ "log<sub>2</sub>". | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | ===Questions=== | ||
<quiz display=simple> | <quiz display=simple> | ||
− | + | {State the following information theoretic quantities "nat": | |
− | { | ||
|type="{}"} | |type="{}"} | ||
− | h(X) | + | $h(X) \ = \ $ { -2.06--1.94 } nat |
− | h(Y) | + | $h(Y) \ \hspace{0.03cm} = \ $ { 1 3% } nat |
− | h(XY) | + | $h(XY)\ \hspace{0.17cm} = \ $ { -1.55--1.45 } nat |
− | I(X;Y) | + | $I(X;Y)\ = \ $ { 0.5 3% } nat |
− | { | + | {What are the same quantities with the pseudo–unit "bit"? |
|type="{}"} | |type="{}"} | ||
− | h(X) | + | $h(X) \ = \ $ { -2.986--2.786 } bit |
− | h(Y) | + | $h(Y) \ \hspace{0.03cm} = \ $ { 1.443 3% } bit |
− | h(XY) | + | $h(XY)\ \hspace{0.17cm} = \ $ { -2.22--2.10 } bit |
− | I(X;Y) | + | $I(X;Y)\ = \ $ { 0.721 3% } bit |
− | { | + | {Calculate the conditional differential entropy $h(Y|X)$. |
|type="{}"} | |type="{}"} | ||
− | h(Y|X) | + | $h(Y|X) \ = \ $ { 0.5 3% } nat |
− | h(Y|X) | + | $h(Y|X) \ = \ $ { 0.721 3% } bit |
− | { | + | {Calculate the conditional differential entropy $h(X|Y)$. |
|type="{}"} | |type="{}"} | ||
− | h(X|Y) | + | $h(X|Y) \ = \ $ { -2.6--2.4 } nat |
− | h(X|Y) | + | $h(X|Y) \ = \ $ { -3.7--3.5 } bit |
− | { | + | {Which of the following quantities is never negative? |
|type="[]"} | |type="[]"} | ||
− | + | + | + 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. |
+ | |||
</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | + | '''(1)''' Since the results are required in "nat", it is convenient to use the natural logarithm: | |
− | + | *The random variable $X is uniformly distributed between 0 and 1/{\rm e}^2={\rm e}^{-2}$: | |
− | $$h(X) = {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}{\rm e}^{-2}\hspace{0.05cm}) | + | :$$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}. $$ | \hspace{0.15cm}\underline{= -2\,{\rm nat}}\hspace{0.05cm}. $$ | ||
− | + | *The random variable $Y is triangularly distributed between ±{\rm e}^{-0.5}$: | |
− | $$h(Y) = {\rm ln} \hspace{0.1cm} (\hspace{0.05cm}\sqrt{ {\rm e} } \cdot \sqrt{ {\rm e} } ) | + | :$$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}.$$ | \hspace{0.15cm}\underline{= +1\,{\rm nat}}\hspace{0.05cm}.$$ | ||
− | + | * The area of the parallelogram is given by | |
− | F=A⋅B=e−2⋅e0.5=e−1.5. | + | :F=A⋅B=e−2⋅e0.5=e−1.5. |
− | + | *Thus, the 2D-PDF in the area highlighted in green has constant height $C = 1/F ={\rm e}^{1.5}$ 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}.$$ | \hspace{0.15cm}\underline{= -1.5\,{\rm nat}}\hspace{0.05cm}.$$ | ||
− | + | *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_. |
− | + | ||
− | h(X) = −2nat0.693nat/bit=−2.886bit_, | + | |
− | h(Y) = +1nat0.693nat/bit=+1.443bit_, | + | |
− | h(XY) = −1.5nat0.693nat/bit=−2.164bit_, | + | '''(2)''' In general, the relation $\log_2(x) = \ln(x)/\ln(2)$ holds. Thus, using the results of subtask '''(1)''', we obtain: |
− | I(X;Y) = 0.5nat0.693nat/bit=0.721bit_. | + | :h(X) = −2nat0.693nat/bit=−2.886bit_, |
− | + | :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_, |
− | + | :I(X;Y) = 0.5nat0.693nat/bit=0.721bit_. | |
− | h(Y∣X)=h(Y)−I(X;Y)=1nat−0.5nat=0.5nat=0.721bit_. | + | *Or also: |
− | + | :I(X;Y)=−2.886bit+1.443bit+2.164bit=0.721bit. | |
− | h(X∣Y)=h(X)−I(X;Y)=−2nat−0.5nat=−2.5nat=−3.607bit_. | + | |
− | + | ||
+ | |||
+ | '''(3)''' The mutual information can also be written in the form $I(X;Y) = h(Y)-h(Y \hspace{-0.05cm}\mid \hspace{-0.05cm} X) $ : | ||
+ | :h(Y∣X)=h(Y)−I(X;Y)=1nat−0.5nat=0.5nat=0.721bit_. | ||
+ | |||
+ | |||
+ | |||
+ | '''(4)''' For the differential inference entropy, it holds correspondingly: | ||
+ | :h(X∣Y)=h(X)−I(X;Y)=−2nat−0.5nat=−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. | ||
+ | *Arrows pointing up indicate a positive contribution, arrows pointing down indicate a negative contribution. | ||
+ | |||
+ | |||
− | + | '''(5)''' Correct are the <u>proposed solutions 1 to 3</u>. | |
− | + | ||
− | + | Again for clarification: | |
− | + | * For the mutual information $I(X;Y) \ge 0$ 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: | + | [[Category:Information Theory: Exercises|^4.2 AWGN and Value-Continuous Input^]] |
Latest revision as of 10:27, 11 October 2021
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(B⋅√e),
- h(XY)=log(F)=log(A⋅B).
In this exercise, the parameter values A=e−2 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
Solution
- The random variable X is uniformly distributed between 0 and 1/e2=e−2:
- h(X)=ln(e−2)=−2nat_.
- The random variable Y is triangularly distributed between ±e−0.5:
- h(Y)=ln(√e⋅√e)=ln(e)=+1nat_.
- The area of the parallelogram is given by
- F=A⋅B=e−2⋅e0.5=e−1.5.
- 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(e−1.5)=−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(Y∣X) :
- h(Y∣X)=h(Y)−I(X;Y)=1nat−0.5nat=0.5nat=0.721bit_.
(4) For the differential inference entropy, it holds correspondingly:
- h(X∣Y)=h(X)−I(X;Y)=−2nat−0.5nat=−2.5nat=−3.607bit_.
- 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.