Difference between revisions of "Aufgaben:Exercise 4.4: Maximum–a–posteriori and Maximum–Likelihood"

From LNTwww
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[[File:EN_Dig_A_4_4.png|right|frame|Channel transition probabilities]]
 
[[File:EN_Dig_A_4_4.png|right|frame|Channel transition probabilities]]
To illustrate MAP and ML decision, we now construct a very simple example with only two possible messages  $m_0 = 0$  and  $m_1 = 1$, represented by the signal values  $s_0$  and  $s_1$,  respectively:
+
To illustrate  "maximum–a–posteriori"  $\rm (MAP)$  and  "maximum likelihood"  $\rm (ML)$   decision,  we now construct a very simple example with only two possible messages  $m_0 = 0$  and  $m_1 = 1$,  represented by the signal values  $s_0$  resp.  $s_1$:
 
:$$s \hspace{-0.15cm} \ = \ \hspace{-0.15cm}s_0 = +1 \hspace{0.2cm} \Longleftrightarrow \hspace{0.2cm}m = m_0 = 0\hspace{0.05cm},$$
 
:$$s \hspace{-0.15cm} \ = \ \hspace{-0.15cm}s_0 = +1 \hspace{0.2cm} \Longleftrightarrow \hspace{0.2cm}m = m_0 = 0\hspace{0.05cm},$$
 
:$$s \hspace{-0.15cm} \ = \ \hspace{-0.15cm}s_1 = -1 \hspace{0.2cm} \Longleftrightarrow \hspace{0.2cm}m = m_1 = 1\hspace{0.05cm}.$$
 
:$$s \hspace{-0.15cm} \ = \ \hspace{-0.15cm}s_1 = -1 \hspace{0.2cm} \Longleftrightarrow \hspace{0.2cm}m = m_1 = 1\hspace{0.05cm}.$$
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After transmission, the transmitted message is to be estimated by an optimal receiver. Available are:
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After transmission, the message is to be estimated by an optimal receiver.  Available are:
* the  '''maximum likelihood receiver'''  (ML receiver), which does not know the occurrence probabilities  ${\rm Pr}(s = s_i)$,  with the decision rule:
+
* the  '''maximum likelihood receiver'''  $\rm (ML$  receiver$)$,  which does not know the occurrence probabilities  ${\rm Pr}(s = s_i)$,  with the decision rule:
 
:$$\hat{m}_{\rm ML} = {\rm arg} \max_i \hspace{0.1cm} \big[ p_{r |s } \hspace{0.05cm} (\rho  
 
:$$\hat{m}_{\rm ML} = {\rm arg} \max_i \hspace{0.1cm} \big[ p_{r |s } \hspace{0.05cm} (\rho  
 
|s_i ) \big]\hspace{0.05cm},$$
 
|s_i ) \big]\hspace{0.05cm},$$
  
* the  '''maximum-a-posteriori receiver'''  (MAP receiver); this receiver also considers the symbol probabilities of the source in its decision process:
+
* the  '''maximum-a-posteriori receiver'''$\rm (MAP$  receiver$)$;  this receiver also considers the symbol probabilities of the source in its decision process:
 
:$$\hat{m}_{\rm MAP} = {\rm arg} \max_i \hspace{0.1cm} \big[ {\rm Pr}( s = s_i) \cdot p_{r |s } \hspace{0.05cm} (\rho  
 
:$$\hat{m}_{\rm MAP} = {\rm arg} \max_i \hspace{0.1cm} \big[ {\rm Pr}( s = s_i) \cdot p_{r |s } \hspace{0.05cm} (\rho  
 
|s_i ) \big ]\hspace{0.05cm}.$$
 
|s_i ) \big ]\hspace{0.05cm}.$$
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 +
Notes:
 +
*The exercise belongs to the chapter   [[Digital_Signal_Transmission/Optimal_Receiver_Strategies|"Optimal Receiver Strategies"]].
  
 +
*Reference is also made to the chapter  [[Digital_Signal_Transmission/Structure_of_the_Optimal_Receiver|"Structure of the Optimal Receiver]].
  
''Notes:''
+
* The necessary statistical principles can be found in the chapter  [[Theory_of_Stochastic_Signals/Statistical_Dependence_and_Independence| "Statistical Dependence and Independence"]]  of the book  "Theory of Stochastic Signals".  
*The exercise belongs to the chapter   [[Digital_Signal_Transmission/Optimal_Receiver_Strategies|"Optimal Receiver Strategies"]].
 
*Reference is also made to the chapter  [[Digital_Signal_Transmission/Structure_of_the_Optimal_Receiver|"Structure of the Optimal Receiver]].
 
* The necessary statistical principles can be found in the chapter  [[Theory_of_Stochastic_Signals/Statistical_Dependence_and_Independence| "Statistical Dependence and Independence"]]  of the book "Theory of Stochastic Signals".  
 
 
   
 
   
  
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{Calculate the symbol error probability of the  '''ML receiver'''.
 
{Calculate the symbol error probability of the  '''ML receiver'''.
 
|type="{}"}
 
|type="{}"}
${\rm Pr(symbol error)}\ = \ $  { 0.15 3% }
+
${\rm Pr(symbol\hspace{0.15cm} error)}\ = \ $  { 0.15 3% }
  
 
{Calculate the symbol error probability of the  '''MAP receiver'''.
 
{Calculate the symbol error probability of the  '''MAP receiver'''.
 
|type="{}"}
 
|type="{}"}
${\rm Pr(symbol error)}\ = \ $ { 0.1 3% }
+
${\rm Pr(symbol\hspace{0.15cm}error)}\ = \ $ { 0.1 3% }
 
</quiz>
 
</quiz>
  

Revision as of 16:24, 15 July 2022

Channel transition probabilities

To illustrate  "maximum–a–posteriori"  $\rm (MAP)$  and  "maximum likelihood"  $\rm (ML)$  decision,  we now construct a very simple example with only two possible messages  $m_0 = 0$  and  $m_1 = 1$,  represented by the signal values  $s_0$  resp.  $s_1$:

$$s \hspace{-0.15cm} \ = \ \hspace{-0.15cm}s_0 = +1 \hspace{0.2cm} \Longleftrightarrow \hspace{0.2cm}m = m_0 = 0\hspace{0.05cm},$$
$$s \hspace{-0.15cm} \ = \ \hspace{-0.15cm}s_1 = -1 \hspace{0.2cm} \Longleftrightarrow \hspace{0.2cm}m = m_1 = 1\hspace{0.05cm}.$$
  • Let the probabilities of occurrence be:
$${\rm Pr}(s = s_0) = 0.75,\hspace{0.2cm}{\rm Pr}(s = s_1) = 0.25 \hspace{0.05cm}.$$
  • The received signal can – for whatever reason – take three different values, i.e.
$$r = +1,\hspace{0.2cm}r = 0,\hspace{0.2cm}r = -1 \hspace{0.05cm}.$$
  • The conditional channel probabilities can be taken from the graph.


After transmission, the message is to be estimated by an optimal receiver.  Available are:

  • the  maximum likelihood receiver  $\rm (ML$  receiver$)$,  which does not know the occurrence probabilities  ${\rm Pr}(s = s_i)$,  with the decision rule:
$$\hat{m}_{\rm ML} = {\rm arg} \max_i \hspace{0.1cm} \big[ p_{r |s } \hspace{0.05cm} (\rho |s_i ) \big]\hspace{0.05cm},$$
  • the  maximum-a-posteriori receiver$\rm (MAP$  receiver$)$;  this receiver also considers the symbol probabilities of the source in its decision process:
$$\hat{m}_{\rm MAP} = {\rm arg} \max_i \hspace{0.1cm} \big[ {\rm Pr}( s = s_i) \cdot p_{r |s } \hspace{0.05cm} (\rho |s_i ) \big ]\hspace{0.05cm}.$$



Notes:



Questions

1

With which probabilities do the received values occur?

${\rm Pr}(r = +1) \ = \ $

${\rm Pr}(r = -1) \ = \ $

${\rm Pr}(r = 0) \hspace{0.45cm} = \ $

2

Calculate all inference probabilities.

${\rm Pr}(s_0|r = +1) \ = \ $

${\rm Pr}(s_1|r = +1) \ = \ $

${\rm Pr}(s_0|r = -1) \ = \ $

${\rm Pr}(s_1|r = -1) \ = \ $

${\rm Pr}(s_0|r = 0) \hspace{0.45cm} = \ $

${\rm Pr}(s_1|r = 0) \hspace{0.45cm} = \ $

3

Do MAP and ML receivers differ under the condition  "$r = +1$"?

yes,
no.

4

Do MAP and ML receivers differ under the condition  "$r = -1$"?

yes,
no.

5

Which statements are true under the condition  "$r = 0$"?

The MAP receiver decides for  $s_0$.
The MAP receiver decides for  $s_1$.
The ML receiver decides for  $s_0$.
The ML receiver decides for  $s_1$.

6

Calculate the symbol error probability of the  ML receiver.

${\rm Pr(symbol\hspace{0.15cm} error)}\ = \ $

7

Calculate the symbol error probability of the  MAP receiver.

${\rm Pr(symbol\hspace{0.15cm}error)}\ = \ $


Solution

(1)  The receiver side occurrence probabilities we are looking for are

$${\rm Pr} ( r = +1) \hspace{-0.1cm} \ = \ \hspace{-0.1cm} {\rm Pr} ( s_0) \cdot {\rm Pr} ( r = +1 \hspace{0.05cm}| \hspace{0.05cm}s = +1) = 0.75 \cdot 0.8 \hspace{0.05cm}\hspace{0.15cm}\underline { = 0.6}\hspace{0.05cm},$$
$${\rm Pr} ( r = -1) \hspace{-0.1cm} \ = \ \hspace{-0.1cm} {\rm Pr} ( s_1) \cdot {\rm Pr} ( r = -1 \hspace{0.05cm}| \hspace{0.05cm}s = -1) = 0.25 \cdot 0.6 \hspace{0.05cm}\hspace{0.15cm}\underline {= 0.15}\hspace{0.05cm},$$
$${\rm Pr} ( r = 0) \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 1 - {\rm Pr} ( r = +1) - {\rm Pr} ( r = -1) = 1 - 0.6 - 0.15 \hspace{0.05cm}\hspace{0.15cm}\underline {= 0.25}\hspace{0.05cm}.$$

For the last probability also holds:

$${\rm Pr} ( r = 0) = 0.75 \cdot 0.2 + 0.25 \cdot 0.4 = 0.25\hspace{0.05cm}.$$


(2)  For the first inference probability we are looking for holds:

$${\rm Pr} (s_0 \hspace{0.05cm}| \hspace{0.05cm}r = +1) = \frac{{\rm Pr} ( r = +1 \hspace{0.05cm}|\hspace{0.05cm}s_0 ) \cdot {\rm Pr} ( s_0)}{{\rm Pr} ( r = +1)} = \frac{0.8 \cdot 0.75}{0.6} \hspace{0.05cm}\hspace{0.15cm}\underline {= 1}\hspace{0.05cm}.$$

Correspondingly, we obtain for the other probabilities:

$${\rm Pr} (s_1 \hspace{0.05cm}| \hspace{0.05cm}r = +1) \hspace{-0.1cm} \ = \ 1 - {\rm Pr} (s_0 \hspace{0.05cm}| \hspace{0.05cm}r = +1) \hspace{0.05cm}\hspace{0.15cm}\underline {= 0}\hspace{0.05cm},$$
$${\rm Pr} (s_0 \hspace{0.05cm}| \hspace{0.05cm}r = -1) \hspace{0.05cm}\hspace{0.15cm}\underline {= 0}\hspace{0.05cm},$$
$${\rm Pr} (s_1 \hspace{0.05cm}| \hspace{0.05cm}r = -1) \hspace{0.05cm}\hspace{0.15cm}\underline {= 1}\hspace{0.05cm},$$
$${\rm Pr} (s_0 \hspace{0.05cm}| \hspace{0.05cm}r = 0) \hspace{-0.1cm} \ = \ \hspace{-0.1cm}\frac{{\rm Pr} ( r = 0 \hspace{0.05cm}|\hspace{0.05cm}s_0 ) \cdot {\rm Pr} ( s_0)}{{\rm Pr} ( r = 0 )}= \frac{0.2 \cdot 0.75}{0.25} \hspace{0.05cm}\hspace{0.15cm}\underline {= 0.6}\hspace{0.05cm},$$
$${\rm Pr} (s_1 \hspace{0.05cm}| \hspace{0.05cm}r = 0) \hspace{-0.1cm} \ = \ \hspace{-0.1cm} 1- {\rm Pr} (s_0 \hspace{0.05cm}| \hspace{0.05cm}r = 0) \hspace{0.05cm}\hspace{0.15cm}\underline {= 0.4} \hspace{0.05cm}.$$


(3)  Let $r = +1$. Then decides

  • the MAP receiver for $s_0$, because ${\rm Pr} (s_0 \hspace{0.05cm}| \hspace{0.05cm}r = +1) = 1 > {\rm Pr} (s_1 \hspace{0.05cm}| \hspace{0.05cm}r = +1)= 0\hspace{0.05cm},$
  • the ML receiver likewise for $s_0$, since ${\rm Pr} ( r = +1 \hspace{0.05cm}| \hspace{0.05cm}s_0) = 0.8 > {\rm Pr} ( r = +1 \hspace{0.05cm}| \hspace{0.05cm}s_1) = 0 \hspace{0.05cm}.$


So the correct answer is NO.


(4)  NO is also true under the condition "$r = \, –1$", since there is no connection between $s_0$ and "$r = \, –1$".


(5)  Solutions 1 and 4 are correct:

  • The MAP receiver will choose event $s_0$, since ${\rm Pr} (s_0 \hspace{0.05cm}| \hspace{0.05cm}r = 0) = 0.6 > {\rm Pr} (s_1 \hspace{0.05cm}| \hspace{0.05cm}r = 0) = 0.4 \hspace{0.05cm}.$
  • In contrast, the ML receiver will choose $s_1$, since ${\rm Pr} ( r = 0 \hspace{0.05cm}| \hspace{0.05cm}s_1) = 0.4 > {\rm Pr} ( r = 0 \hspace{0.05cm}| \hspace{0.05cm}s_0) = 0.2 \hspace{0.05cm}.$


(6)  The maximum likelihood receiver

  • decides for $s_0$ only if $r = +1$,
  • thus makes no error if $s_1$ was sent,
  • only makes an error when "$s_0$" and "$r = 0$" are combined:
$${\rm Pr} ({\rm symbol error} ) = {\rm Pr} ({\cal E } ) = 0.75 \cdot 0.2 \hspace{0.05cm}\hspace{0.15cm}\underline {= 0.15} \hspace{0.05cm}.$$


(7)  The MAP receiver, on the other hand, decides to use $s_0$ when "$r = 0$". So there is a symbol error only in the combination "$s_1$" and "$r = 0$". From this follows:

$${\rm Pr} ({\rm symbol error} ) = {\rm Pr} ({\cal E } ) = 0.25 \cdot 0.4 \hspace{0.05cm}\hspace{0.15cm}\underline {= 0.1} \hspace{0.05cm}.$$
  • The error probability here is lower than for the ML receiver,
  • because now also the different apriori probabilities ${\rm Pr}(s_0)$ and ${\rm Pr}(s_1)$ are considered.