Aufgaben:Exercise 3.12: Path Weighting Function: Difference between revisions
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{{quiz-Header|Buchseite= | {{quiz-Header|Buchseite=Channel_Coding/Distance_Characteristics_and_Error_Probability_Barriers}} | ||
[[File: | [[File:EN_KC_A_3_12.png|right|frame|$m = 1$ convolutional encoder and state transition diagram]] | ||
In [[Aufgaben: | In [[Aufgaben:Exercise_3.6:_State_Transition_Diagram|$\text{Exercise 3.6}$]] the state transition diagram for the drawn convolutional encoder with properties | ||
* Rate $R = 1/2$, | * Rate $R = 1/2$, | ||
* memory $m = 1$, | |||
* transfer function matrix $\mathbf{G}(D) = (1, \, D)$ | |||
was constructed which is shown on the right. | |||
Now, from this state transition diagram | |||
* the path weighting enumerator function $T(X)$, and | |||
* the extended path weighting enumerator function $T_{\rm enh}(X, \, U)$ | |||
can be determined, where $X$ and $U$ are dummy variables. The method is explained in detail in the [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Bounds#Path_weighting_enumerator_function_from_state_transition_diagram|"Theory part"]]. | |||
Finally, from $T(X)$ the [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Bounds#Free_distance_vs._minimum_distance|"free distance"]] $d_{\rm F}$ has to be determined. | |||
* | |||
* | Hints: | ||
* This exercise belongs to the chapter [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Boundss| "Distance Characteristics and Error Probability Bounds"]]. | |||
* Consider the series expansion in the solution | |||
:$$\frac{1}{1-x} = 1 + x + x^2 + x^3 + \hspace{0.05cm}\text{...}\hspace{0.1cm}.$$ | :$$\frac{1}{1-x} = 1 + x + x^2 + x^3 + \hspace{0.05cm}\text{...}\hspace{0.1cm}.$$ | ||
| Line 31: | Line 34: | ||
=== | ===Questions=== | ||
<quiz display=simple> | <quiz display=simple> | ||
{ | {What should be considered when modifying the transition diagram? | ||
|type="[]"} | |type="[]"} | ||
+ | + The state $S_0$ must be split into $S_0$ and $S_0\hspace{0.01cm}'$. | ||
- | - The state $S_1$ must be split into $S_0$ and $S_0\hspace{0.01cm}'$. | ||
+ | + The transition from $S_0$ to $S_1$ must be labeled $U\hspace{-0.05cm}X^2$. | ||
+ | + The transition from $S_1$ to $S_1$ is to be labeled $U\hspace{-0.05cm}X$. | ||
+ | + The transition from $S_1$ to $S_0\hspace{0.01cm}'$ shall be labeled $X$. | ||
{ | {What equations apply to the extended path weighting enumerator function $T_{\rm enh}(X, \, U)$? | ||
|type="[]"} | |type="[]"} | ||
- $T_{\rm enh}(X, \, U) = U^2X^3$ | - $T_{\rm enh}(X, \, U) = U^2X^3$ | ||
| Line 47: | Line 50: | ||
+ $T_{\rm enh}(X, \, U) = UX^3 + U^2X^4 + U^3X^5 + \hspace{0.05cm}\text{...}\hspace{0.1cm}$ | + $T_{\rm enh}(X, \, U) = UX^3 + U^2X^4 + U^3X^5 + \hspace{0.05cm}\text{...}\hspace{0.1cm}$ | ||
{ | {What equations apply to the "simple path weighting enumerator function" $T(X)$? | ||
|type="[]"} | |type="[]"} | ||
+ $T(X) = X^3/(1 \, –X)$, | + $T(X) = X^3/(1 \, –X)$, | ||
+ $T(X) = X^3 + X^4 + X^5 +\hspace{0.05cm}\text{...}\hspace{0.1cm}$ | + $T(X) = X^3 + X^4 + X^5 +\hspace{0.05cm}\text{...}\hspace{0.1cm}$ | ||
{ | {What is the free distance of the considered code? | ||
|type="{}"} | |type="{}"} | ||
$d_{\rm F} \ = \ ${ 3 } | $d_{\rm F} \ = \ ${ 3 } | ||
</quiz> | </quiz> | ||
=== | ===Solution=== | ||
{{ML-Kopf}} | {{ML-Kopf}} | ||
[[File: | [[File:EN_KC_A_3_12a.png|right|frame|State transition diagram <br>after modifications]] | ||
'''(1)''' | '''(1)''' From the adjacent graph, one can see that <u>proposed solutions 1, 3, 4, and 5</u> are correct: | ||
* | *The state $S_0$ must be split into a start state $S_0$ and a final state ${S_0}'$. | ||
* | |||
* | *The reason for this is that for the following calculation of the path weighting enumerator function $T(X, \, U)$ all transitions from $S_0$ to $S_0$ must be excluded. | ||
* | |||
*Each encoded symbol $x ∈ \{0, \, 1\}$ is represented by $X^x$, where $X$ is a dummy variable with respect to the output sequence: $x = 0 \ \Rightarrow \ X^0 = 1, \ x = 1 \ \Rightarrow \ X^1 = X. $ It further follows | |||
:$$(00) \ \Rightarrow \ 1, \ (01) \ \Rightarrow \ X, \ (10) \ \Rightarrow \ X, \ (11) \ \Rightarrow \ X^2.$$ | |||
*For a blue transition in the original diagram $($this represents $u_i = 1)$ add the factor $U$ in the modified diagram. | |||
'''(2)''' | '''(2)''' Correct are the <u>proposed solutions 2 und 3</u>: | ||
* | *The reduced diagram is a "ring" according to the listing in the [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Bounds#Rules_for_manipulating_the_state_transition_diagram|"Theory section"]]. It follows: | ||
:$$T_{\rm enh}(X, U) = \frac{A(X, U) \cdot B(X, U)}{1- C(X, U)} | :$$T_{\rm enh}(X, U) = \frac{A(X, U) \cdot B(X, U)}{1- C(X, U)}\hspace{0.05cm}.$$ | ||
\hspace{0.05cm}.$$ | *With $A(X, \, U) = UX^2, \ B(X, \, U) = X, \ C(X, \, U) = UX$, one obtains with the given series expansion: | ||
* | :$$T_{\rm enh}(X, U) = \frac{U \hspace{0.05cm} X^3}{1- U \hspace{0.05cm} X} = U \hspace{0.05cm} X^3 \cdot \left [ 1 + (U \hspace{0.05cm} X) + (U \hspace{0.05cm} X)^2 +\text{...} \hspace{0.10cm} \right ]\hspace{0.05cm}.$$ | ||
:$$T_{\rm enh}(X, U) = \frac{U \hspace{0.05cm} X^3}{1- U \hspace{0.05cm} X} = U \hspace{0.05cm} X^3 \cdot \left [ 1 + (U \hspace{0.05cm} X) + (U \hspace{0.05cm} X)^2 +\text{...} \hspace{0.10cm} \right ] | |||
\hspace{0.05cm}.$$ | |||
'''(3)''' | '''(3)''' One gets from the extended path weighting enumerator function to $T(X)$ by setting the formal parameter $U = 1$. | ||
* So <u>both proposed solutions</u> are correct. | |||
'''(4)''' | '''(4)''' The free distance $d_{\rm F}$ can be read from the path weighting enumerator function $T(X)$ as the lowest exponent of the dummy variable $X$ ⇒ $d_{\rm F} \ \underline{= 3}$. | ||
{{ML-Fuß}} | {{ML-Fuß}} | ||
[[Category: | [[Category:Channel Coding: Exercises|^3.5 Distance Properties^]] | ||
[[de:Aufgaben:Aufgabe 3.12: Pfadgewichtsfunktion]] | |||
Latest revision as of 17:54, 16 March 2026

In $\text{Exercise 3.6}$ the state transition diagram for the drawn convolutional encoder with properties
- Rate $R = 1/2$,
- memory $m = 1$,
- transfer function matrix $\mathbf{G}(D) = (1, \, D)$
was constructed which is shown on the right.
Now, from this state transition diagram
- the path weighting enumerator function $T(X)$, and
- the extended path weighting enumerator function $T_{\rm enh}(X, \, U)$
can be determined, where $X$ and $U$ are dummy variables. The method is explained in detail in the "Theory part".
Finally, from $T(X)$ the "free distance" $d_{\rm F}$ has to be determined.
Hints:
- This exercise belongs to the chapter "Distance Characteristics and Error Probability Bounds".
- Consider the series expansion in the solution
- $$\frac{1}{1-x} = 1 + x + x^2 + x^3 + \hspace{0.05cm}\text{...}\hspace{0.1cm}.$$
Questions
Solution

after modifications
(1) From the adjacent graph, one can see that proposed solutions 1, 3, 4, and 5 are correct:
- The state $S_0$ must be split into a start state $S_0$ and a final state ${S_0}'$.
- The reason for this is that for the following calculation of the path weighting enumerator function $T(X, \, U)$ all transitions from $S_0$ to $S_0$ must be excluded.
- Each encoded symbol $x ∈ \{0, \, 1\}$ is represented by $X^x$, where $X$ is a dummy variable with respect to the output sequence: $x = 0 \ \Rightarrow \ X^0 = 1, \ x = 1 \ \Rightarrow \ X^1 = X. $ It further follows
- $$(00) \ \Rightarrow \ 1, \ (01) \ \Rightarrow \ X, \ (10) \ \Rightarrow \ X, \ (11) \ \Rightarrow \ X^2.$$
- For a blue transition in the original diagram $($this represents $u_i = 1)$ add the factor $U$ in the modified diagram.
(2) Correct are the proposed solutions 2 und 3:
- The reduced diagram is a "ring" according to the listing in the "Theory section". It follows:
- $$T_{\rm enh}(X, U) = \frac{A(X, U) \cdot B(X, U)}{1- C(X, U)}\hspace{0.05cm}.$$
- With $A(X, \, U) = UX^2, \ B(X, \, U) = X, \ C(X, \, U) = UX$, one obtains with the given series expansion:
- $$T_{\rm enh}(X, U) = \frac{U \hspace{0.05cm} X^3}{1- U \hspace{0.05cm} X} = U \hspace{0.05cm} X^3 \cdot \left [ 1 + (U \hspace{0.05cm} X) + (U \hspace{0.05cm} X)^2 +\text{...} \hspace{0.10cm} \right ]\hspace{0.05cm}.$$
(3) One gets from the extended path weighting enumerator function to $T(X)$ by setting the formal parameter $U = 1$.
- So both proposed solutions are correct.
(4) The free distance $d_{\rm F}$ can be read from the path weighting enumerator function $T(X)$ as the lowest exponent of the dummy variable $X$ ⇒ $d_{\rm F} \ \underline{= 3}$.