Difference between revisions of "Aufgaben:Exercise 3.14: Channel Coding Theorem"
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[[File:EN_Inf_A_3_14.png|right|frame|Information-theoretical quantities of the BSC and EUCmodels]] | [[File:EN_Inf_A_3_14.png|right|frame|Information-theoretical quantities of the BSC and EUCmodels]] | ||
− | [https://en.wikipedia.org/wiki/Claude_Shannon Shannon's] channel coding theorem states that an error-free transmission can be made over a | + | [https://en.wikipedia.org/wiki/Claude_Shannon Shannon's] channel coding theorem states that an error-free transmission can be made over a "discrete memoryless channel" $\rm(DMC)$ with the code rate R as long as R is not greater than the channel capacity |
:C=max | :C = \max_{P_X(X)} \hspace{0.15cm} I(X;Y) \hspace{0.05cm}. | ||
− | The | + | The "Channel Coding Theorem" is to be evaluated numerically in this task, whereby two typical channel models are to be considered: |
− | * | + | * The \rm BSC model ("Binary Symmetric Channel") with error probability ε = 0.25 and channel capacity C = 1 - H_{\rm bin}(ε), |
− | * the \rm EUC model (from | + | * the \rm EUC model (from "Extremely Unsymmetric Channel"; this designation originates from us and is not common) according to [[Aufgaben:Aufgabe_3.11Z:_Extrem_unsymmetrischer_Kanal|Exercise 3.11Z]]. |
− | The graphs show the numerical values of the information-theoretical quantities for the two | + | The graphs show the numerical values of the information-theoretical quantities for the two models \rm BSC and \rm EUC: |
− | * | + | * The source entropy H(X), |
* the equivocation H(X|Y), | * the equivocation H(X|Y), | ||
* the mutual information I(X; Y), | * the mutual information I(X; Y), | ||
− | * the irrelevance H(Y|X), | + | * the irrelevance H(Y|X), |
* the sink entropy H(Y). | * the sink entropy H(Y). | ||
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<quiz display=simple> | <quiz display=simple> | ||
− | {Which statements are true for uncoded transmission ⇒ \underline{R = 1} assuming p_0 = p_1 = 0.5 ? | + | {Which statements are true for uncoded transmission ⇒ \underline{R = 1} assuming p_0 = p_1 = 0.5 ? |
|type="()"} | |type="()"} | ||
- BSC results in a smaller bit error probability. | - BSC results in a smaller bit error probability. | ||
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+ Both models lead to the same bit error probability. | + Both models lead to the same bit error probability. | ||
− | {With \underline{R = 1} | + | {With \underline{R = 1} the result can be (formally) improved by other values of p_0 or $p_1 ? |
|type="()"} | |type="()"} | ||
− | - | + | - With both channels. |
- With the BSC model. | - With the BSC model. | ||
+ With the EUC model. | + With the EUC model. | ||
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{Over which channel can error-free transmission be achieved with the \underline{R = 0.16} ? | {Over which channel can error-free transmission be achieved with the \underline{R = 0.16} ? | ||
|type="()"} | |type="()"} | ||
− | + | + | + With both channels. |
− | - | + | - With the BSC model. |
- With the EUC model. | - With the EUC model. | ||
- With none of the models. | - With none of the models. | ||
Line 60: | Line 60: | ||
|type="()"} | |type="()"} | ||
- Over both channels. | - Over both channels. | ||
− | - | + | - With the BSC model. |
+ With the EUC model. | + With the EUC model. | ||
− | - With | + | - With none of the models. |
{Over which channel can error-free transmission be achieved with the rate \underline{R = 0.48} ? | {Over which channel can error-free transmission be achieved with the rate \underline{R = 0.48} ? | ||
|type="()"} | |type="()"} | ||
− | - | + | - With both channels. |
− | - | + | - With the BSC model. |
- With the EUC model. | - With the EUC model. | ||
+ With none of the models. | + With none of the models. |
Revision as of 12:36, 24 September 2021
Shannon's channel coding theorem states that an error-free transmission can be made over a "discrete memoryless channel" \rm(DMC) with the code rate R as long as R is not greater than the channel capacity
- C = \max_{P_X(X)} \hspace{0.15cm} I(X;Y) \hspace{0.05cm}.
The "Channel Coding Theorem" is to be evaluated numerically in this task, whereby two typical channel models are to be considered:
- The \rm BSC model ("Binary Symmetric Channel") with error probability ε = 0.25 and channel capacity C = 1 - H_{\rm bin}(ε),
- the \rm EUC model (from "Extremely Unsymmetric Channel"; this designation originates from us and is not common) according to Exercise 3.11Z.
The graphs show the numerical values of the information-theoretical quantities for the two models \rm BSC and \rm EUC:
- The source entropy H(X),
- the equivocation H(X|Y),
- the mutual information I(X; Y),
- the irrelevance H(Y|X),
- the sink entropy H(Y).
The parameter in these tables is p_0 = {\rm Pr}(X = 0) in the range between p_0 = 0.3 to p_0 = 0.7.
For the second source symbol probability, p_1 = {\rm Pr}(X = 1) =1 - p_0.
Hints:
- The exercise belongs to the chapter Application to Digital Signal Transmission.
- Reference is made in particular to the page Definition and meaning of channel capacity.
Questions
Solution
- The BSC error probability is ⇒ R = 1 with p_0 = p_1 = 0.5 for uncoded transmission:
- p_{\rm B} = 0.5 \cdot 0.25 + 0.5 \cdot 0.25=0.25 \hspace{0.05cm}.
- Correspondingly, with the same boundary conditions for the EUC model::
- p_{\rm B} = 0.5 \cdot 0 + 0.5 \cdot 0.5=0.25 \hspace{0.05cm}.
(2) Proposed solution 3 is correct:
- In the BSC model with the distortion probability ε = 0.25 , for uncoded transmission ⇒ R = 1 , the bit error probability is equal to p_{\rm B} = 0.25. regardless of p_0 and p_1
- In contrast, with the EUC model, for example, a smaller bit error probability is obtained with p_0 = 0.6 and p_1 = 0.4 :
- p_{\rm B} = 0.6 \cdot 0 + 0.4 \cdot 0.5=0.2 \hspace{0.05cm}.
- Note, however, that now the source entropy is no longer H(X) = 1\ \rm (bit) but only H(X) = H_{bin} (0.6) = 0.971 \ \rm (bit).
- In the limiting case p_0 = 1 , only zeros are transmitted and H(X) = 0. For the bit error probability, however, the following then actually applies:
- p_{\rm B} = 1 \cdot 0 + 0 \cdot 0.5=0 \hspace{0.05cm}.
- So no information is transmitted, but it is transmitted with the bit error probability "zero".
(3) Proposed solution 1 is correct:
- From the graph on the information page, it can be read for the capacities of the two channels:
- C_{\rm BSC} = 0.1887 \ \rm {bit/use}, \hspace{0.5cm}C_{rm EUC} = 0.3219 \ \rm {bit/use}.
- According to the channel coding theorem, a channel coding can be found at R ≤ C with which the error probability can be made zero.
- For both channels this condition is true with rate R = 0.16 .
(4) Proposed solution 3 is correct:
- With the EUC model, the necessary condition R ≤ C for an error-free transmission is fulfilled with R = 0.32 and C = 0.3219
- However, the prerequisite for this is the probability function P_X(X) = (0.6,\ 0.4).
- In contrast, for equally probable symbols ⇒ P_X(X) = (0.5,\ 0.5) the mutual information I(X; Y) = 0.3113 would result,
i.e. a smaller value than for the channel capacity C, and I(X; Y) < R also applies. - It can be seen that the EUC model offers more potential for the application of channel coding than the BSC model. Here, for example, it can be exploited in the code that a transmitted "0" is always transmitted without errors.
(5) The comments on subtasks (3) and (4) show that theproposed solution 4 applies.