Aufgaben:Exercise 3.12: Path Weighting Function: Difference between revisions

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{{quiz-Header|Buchseite=Channel_Coding/Distance_Characteristics_and_Error_Probability_Barriers}}
{{quiz-Header|Buchseite=Channel_Coding/Distance_Characteristics_and_Error_Probability_Barriers}}


[[File:EN_KC_A_3_12.png|right|frame|Convolutional encoder with  $m = 1$  and state transition diagram]]
[[File:EN_KC_A_3_12.png|right|frame|$m = 1$  convolutional encoder and state transition diagram]]
In  [[Aufgaben:Exercise_3.6:_State_Transition_Diagram|"Exercise 3.6"]]  the state transition diagram for the drawn convolutional encoder was constructed with properties
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  $m = 1$,
* memory  $m = 1$,
* transfer function matrix  $\mathbf{G}(D) = (1, \, D)$
* transfer function matrix  $\mathbf{G}(D) = (1, \, D)$




which is shown on the right.
was constructed  which is shown on the right.
 
Now,   from this state transition diagram
* the path weighting enumerator function  $T(X)$,  and


Now, from this state transition diagram
* the path weighting enumerator function  $T(X)$, and
* the extended path weighting enumerator function  $T_{\rm enh}(X, \, U)$
* the extended path weighting enumerator function  $T_{\rm enh}(X, \, U)$




be determined, where  $X$  and  $U$  are dummy variables.  
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_Barriers#Path_weighting_enumerator_function_from_state_transition_diagram|"Theory part"]].  
 
The procedure is explained in detail in the  [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Barriers#Path_weighting_enumerator_function_from_state_transition_diagram|"Theory part"]]  to this chapter. Finally, from  $T(X)$  the  [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Barriers#Free_distance_vs._minimum_distance|"free distance"]]  $d_{\rm F}$  has to be determined.
 
 


Finally, from  $T(X)$  the  [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Barriers#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"]].


Hints:
* This exercise belongs to the chapter  [[Channel_Coding/Distance_Characteristics_and_Error_Probability_Barriers| "Distance Characteristics and Error Probability Barriers"]].
* Consider the series expansion in the solution
* Consider the series expansion in the solution
:$$\frac{1}{1-x} = 1 + x + x^2 +  x^3 + \hspace{0.05cm}\text{...}\hspace{0.1cm}.$$
:$$\frac{1}{1-x} = 1 + x + x^2 +  x^3 + \hspace{0.05cm}\text{...}\hspace{0.1cm}.$$
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|type="[]"}
|type="[]"}
+ The state  $S_0$  must be split into  $S_0$  and  $S_0\hspace{0.01cm}'$.
+ 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 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  $S_0$  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  $S_1$  is to be labeled  $U\hspace{-0.05cm}X$.
+ The transition from  $S_1$  to  $S_0\hspace{0.01cm}'$ shall be labeled $X$.
+ 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  $T_{\rm enh}(X, \, U)$?
{What equations apply to the extended path weighting enumerator function  $T_{\rm enh}(X, \, U)$?
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+ $T_{\rm enh}(X, \, U) = UX^3 + U^2X^4 + U^3X^5 + \hspace{0.05cm}\text{...}\hspace{0.1cm}$
+ $T_{\rm enh}(X, \, U) = UX^3 + U^2X^4 + U^3X^5 + \hspace{0.05cm}\text{...}\hspace{0.1cm}$


{What equations apply to the "simple" path weighting enumerator function  $T(X)$?
{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 code under consideration?
{What is the free distance of the considered code?
|type="{}"}
|type="{}"}
$d_{\rm F} \ = \ ${ 3 }
$d_{\rm F} \ = \ ${ 3 }

Revision as of 17:55, 22 November 2022

$m = 1$  convolutional encoder and state transition diagram

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:

  • 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

1 What should be considered when modifying the transition diagram?

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  $X$.

2 What equations apply to the extended path weighting enumerator function  $T_{\rm enh}(X, \, U)$?

$T_{\rm enh}(X, \, U) = U^2X^3$
$T_{\rm enh}(X, \, U) = UX^3/(1 \, –UX)$
$T_{\rm enh}(X, \, U) = UX^3 + U^2X^4 + U^3X^5 + \hspace{0.05cm}\text{...}\hspace{0.1cm}$

3 What equations apply to the  "simple path weighting enumerator function"  $T(X)$?

$T(X) = X^3/(1 \, –X)$,
$T(X) = X^3 + X^4 + X^5 +\hspace{0.05cm}\text{...}\hspace{0.1cm}$

4 What is the free distance of the considered code?

$d_{\rm F} \ = \ $


Solution

State transition diagram 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 code 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)  Richtig sind die Lösungsvorschläge 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 $X$   ⇒   $d_{\rm F} \ \underline{= 3}$.