Difference between revisions of "Aufgaben:Exercise 4.06: Optimal Decision Boundaries"
(26 intermediate revisions by 5 users not shown) | |||
Line 1: | Line 1: | ||
− | {{quiz-Header|Buchseite= | + | {{quiz-Header|Buchseite=Digital_Signal_Transmission/Approximation_of_the_Error_Probability}} |
− | [[File:P_ID2015__Dig_A_4_6.png|right|frame| | + | [[File:P_ID2015__Dig_A_4_6.png|right|frame|Signal space constellation with<br> $N = 2, \ M = 2$]] |
− | + | We consider a binary transmission system $(M = 2)$ that is defined by the drawn two-dimensional signal space constellation $(N = 2)$. The following applies to the two possible transmitted vectors that are directly coupled to the messages $m_0$ and $m_1$: | |
− | :$$\boldsymbol{ s }_0 \hspace{-0.1cm} \ =\ \hspace{-0.1cm} \sqrt {E} \cdot (1,\hspace{0.1cm} 5) \hspace{0.2cm} \ | + | :$$\boldsymbol{ s }_0 \hspace{-0.1cm} \ =\ \hspace{-0.1cm} \sqrt {E} \cdot (1,\hspace{0.1cm} 5) \hspace{0.2cm} \Leftrightarrow \hspace{0.2cm} m_0 \hspace{0.05cm},$$ |
− | :$$ \boldsymbol{ s }_1 \hspace{-0.1cm} \ =\ \hspace{-0.1cm} \sqrt {E} \cdot (4, \hspace{0.1cm}1) \hspace{0.2cm} \ | + | :$$ \boldsymbol{ s }_1 \hspace{-0.1cm} \ =\ \hspace{-0.1cm} \sqrt {E} \cdot (4, \hspace{0.1cm}1) \hspace{0.2cm} \Leftrightarrow \hspace{0.2cm} m_1 \hspace{0.05cm}.$$ |
− | + | The optimal decision boundary between the regions $I_0 ⇔ m_0$ and $I_1 ⇔ m_1$ is sought. The following assumptions are made: | |
− | * | + | * It applies to subtasks '''(1)''' to '''(3)''': |
:$${\rm Pr}(m_0 ) = {\rm Pr}(m_1 ) = 0.5 | :$${\rm Pr}(m_0 ) = {\rm Pr}(m_1 ) = 0.5 | ||
\hspace{0.05cm}. $$ | \hspace{0.05cm}. $$ | ||
− | * | + | * For subtasks '''(4)''' and '''(5)''' should apply: |
:$${\rm Pr}(m_0 ) = 0.817 \hspace{0.05cm},\hspace{0.2cm} {\rm Pr}(m_1 ) = 0.183\hspace{0.3cm} | :$${\rm Pr}(m_0 ) = 0.817 \hspace{0.05cm},\hspace{0.2cm} {\rm Pr}(m_1 ) = 0.183\hspace{0.3cm} | ||
\Rightarrow \hspace{0.3cm} {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | \Rightarrow \hspace{0.3cm} {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | ||
Line 17: | Line 17: | ||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | + | For AWGN noise with variance $\sigma_n^2$, the decision boundary is the solution of the following vectorial equation with respect to the vector $\boldsymbol{ \rho } =(\rho_1, \rho_2)$: | |
:$$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 + 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | :$$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 + 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | ||
− | 2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm} | + | 2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$ |
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | + | In addition, two received values | |
+ | :$$\boldsymbol{ A }= \sqrt {E} \cdot (1.5, \hspace{0.1cm}2)\hspace{0.05cm},$$ | ||
+ | :$$\boldsymbol{ B }= \sqrt {E} \cdot (3, \hspace{0.1cm}3.5) $$ | ||
− | + | are drawn in the graphic. It must be checked whether these should be assigned to the regions $I_0$ $($and thus the message $m_0)$ or to $I_1$ $($message $m_1)$ given the corresponding boundary conditions. | |
− | |||
− | |||
+ | Notes: | ||
+ | * The exercise belongs to the chapter [[Digital_Signal_Transmission/Approximation_of_the_Error_Probability|"Approximation of the Error Probability"]]. | ||
+ | |||
+ | * For numeric calculations, the energy $E = 1$ can be set for simplification. | ||
+ | |||
− | === | + | ===Questions=== |
<quiz display=simple> | <quiz display=simple> | ||
− | { | + | {Where lies the optimal decision boundary for equally probable symbols? At |
|type="[]"} | |type="[]"} | ||
+ $\rho_2 = 3/4 \cdot \rho_1 + 9/8$, | + $\rho_2 = 3/4 \cdot \rho_1 + 9/8$, | ||
Line 50: | Line 45: | ||
- $\rho_2 = 3$. | - $\rho_2 = 3$. | ||
− | { | + | {To which decision area does the received value $A = (1.5, \ \, 2)$ belong? |
|type="()"} | |type="()"} | ||
− | - | + | - To decision area $I_0$, |
− | + | + | + to decision area $I_1$. |
− | { | + | {To which decision area does the received value $B = (3, \ \, 3.5)$ belong? |
|type="()"} | |type="()"} | ||
− | + | + | + To decision area $I_0$, |
− | - | + | - to decision area $I_1$. |
− | { | + | {What is the equation of the decision line for ${\rm Pr}(m_0) = 0.817, \ \sigma_n = 1$? |
− | |type=" | + | |type="()"} |
- $\rho_2 = 3/4 \cdot \rho_1 + 9/8$, | - $\rho_2 = 3/4 \cdot \rho_1 + 9/8$, | ||
+ $\rho_2 = 3/4 \cdot \rho_1 + 3/4$, | + $\rho_2 = 3/4 \cdot \rho_1 + 3/4$, | ||
− | - $\rho_2 = 3/4 \cdot \ | + | - $\rho_2 = 3/4 \cdot \rho_1 + 3/2$, |
− | - $\rho_2 = 3/4$. | + | - $\rho_2 = 3/4 \cdot \rho_1$. |
− | { | + | {Which decisions are made with these new regions $I_0$ and $I_1$? |
|type="[]"} | |type="[]"} | ||
− | + | + | + The received vector $A$ is interpreted as message $m_0$. |
− | - | + | - The received vector $A$ is interpreted as message $m_1$. |
− | + | + | + The received vector $B$ is interpreted as message $m_0$. |
− | - | + | - The received vector $B$ is interpreted as message $m_1$. |
</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | '''(1)''' | + | '''(1)''' With ${\rm Pr}(m_0) = {\rm Pr}(m_1) = 0.5$, the equation of the boundary line between the decision areas $I_0$ and $I_1$ reads: |
:$$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 = | :$$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 = | ||
2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$ | 2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$ | ||
− | + | *With the given vector values, i.e. the numerical values | |
:$$|| \boldsymbol{ s }_1||^2 = 4^2 + 1^2 = 17\hspace{0.05cm}, \hspace{0.2cm} | :$$|| \boldsymbol{ s }_1||^2 = 4^2 + 1^2 = 17\hspace{0.05cm}, \hspace{0.2cm} | ||
|| \boldsymbol{ s }_0||^2 = 1^2 + 5^2 = 26\hspace{0.05cm}, \hspace{0.2cm} | || \boldsymbol{ s }_0||^2 = 1^2 + 5^2 = 26\hspace{0.05cm}, \hspace{0.2cm} | ||
\boldsymbol{ s }_1 - \boldsymbol{ s }_0 = (3,\hspace{0.1cm}-4) \hspace{0.05cm}$$ | \boldsymbol{ s }_1 - \boldsymbol{ s }_0 = (3,\hspace{0.1cm}-4) \hspace{0.05cm}$$ | ||
− | + | :one obtains the following equation for the decision boundaries: | |
:$$3 \cdot \rho_1 - 4 \cdot \rho_2 = ({17-26})/{2} = - {9}/{2} | :$$3 \cdot \rho_1 - 4 \cdot \rho_2 = ({17-26})/{2} = - {9}/{2} | ||
\hspace{0.3cm}\Rightarrow \hspace{0.3cm}\rho_2 = 3/4 \cdot \rho_1 + 9/8 | \hspace{0.3cm}\Rightarrow \hspace{0.3cm}\rho_2 = 3/4 \cdot \rho_1 + 9/8 | ||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | [[File:P_ID2033__Dig_A_4_6a.png|right|frame| | + | [[File:P_ID2033__Dig_A_4_6a.png|right|frame|Decision regions for $K=0$]] |
− | + | *The decision line lies in the middle between $s_0$ and $s_1$ and is rotated by $90^\circ$ compared to the connecting line between the two symbols. It goes through the point $(2.5, \ \, 3)$. So the <u>first solution</u> is correct. | |
+ | |||
+ | *Solution 2, on the other hand, describes the connecting line itself and $\rho_2 = 3$ is a horizontal line. | ||
+ | |||
− | |||
+ | '''(2)''' The decision region $I_1$ should of course contain the point $s_1$ ⇒ region below the decision line. | ||
+ | *Point $A = (1.5, \ \, 2)$ belongs to this decision region, as shown in the graphic. | ||
+ | *This can be shown mathematically, since the decision line goes through the point $(1.5, \ \, 2.25)$, for example, and thus $(1.5, \ \, 2)$ lies below the decision line. | ||
+ | *So, <u>solution 2</u> is correct. | ||
− | |||
− | '''(3)''' | + | '''(3)''' The decision line also goes through the point $(3, \ \, 3.375)$. |
+ | *$B = (3, \ \, 3.5)$ lies above and therefore belongs to the decision region $I_0$ according to <u>solution 1</u>. | ||
− | '''(4)''' | + | '''(4)''' According to the equation in the information section and the calculations for subtask '''(1)''', the following now applies: |
+ | [[File:P_ID2034__Dig_A_4_6c.png|right|frame|Decision regions for different $K$]] | ||
:$$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 + 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | :$$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 + 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | ||
2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$ | 2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$ | ||
− | + | *With $|| \boldsymbol{ s }_1||^2 = 17$, $|| \boldsymbol{ s }_0||^2 = 26$, $ \boldsymbol{ s }_1 \, –\boldsymbol{ s }_0 = (3, \ \, –4)$ we obtain: | |
:$$\rho_2 = 3/4 \cdot \rho_1 + 9/8 - K /8 | :$$\rho_2 = 3/4 \cdot \rho_1 + 9/8 - K /8 | ||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | + | *The following abbreviation was used here: | |
:$$K = 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | :$$K = 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = | ||
2 \cdot 1^2 \cdot 1.5 = 3 \hspace{0.05cm}.$$ | 2 \cdot 1^2 \cdot 1.5 = 3 \hspace{0.05cm}.$$ | ||
− | + | *From this it follows: | |
:$$\rho_2 = 3/4 \cdot \rho_1 + 9/8 - 3 /8 = 3/4 \cdot \rho_1 + 3/4 | :$$\rho_2 = 3/4 \cdot \rho_1 + 9/8 - 3 /8 = 3/4 \cdot \rho_1 + 3/4 | ||
\hspace{0.05cm}.$$ | \hspace{0.05cm}.$$ | ||
− | + | *The decision line is shifted down by $3/8$ $($black curve, labeled "$K = 3$" in the graphic$)$. So, <u>solution 2</u> is correct. | |
− | + | #The first equation describes the optimal decision line for equally probable symbols $(K = 0$, dashed gray$)$. | |
+ | #The third equation is valid for $K = \, –3$. This results with $\sigma_n^2 = 1$ for the symbol probabilities ${\rm Pr}(m_1) \approx 0.817$ and ${\rm Pr}(m_0) \approx 0.138$ (green curve). | ||
+ | #The violet straight line results with $K = 9$, i.e. with the same probabilities as for the black curve, but now with the variance $\sigma_n^2 = 3$. | ||
− | '''(5)''' | + | '''(5)''' The graphic above already shows that both $A$ and $B$ now belong to the decision region $I_0$. <u>Solutions 1 and 3</u> are correct. |
{{ML-Fuß}} | {{ML-Fuß}} | ||
− | [[Category: | + | [[Category:Digital Signal Transmission: Exercises|^4.3 BER Approximation^]] |
Latest revision as of 15:57, 1 October 2022
We consider a binary transmission system $(M = 2)$ that is defined by the drawn two-dimensional signal space constellation $(N = 2)$. The following applies to the two possible transmitted vectors that are directly coupled to the messages $m_0$ and $m_1$:
- $$\boldsymbol{ s }_0 \hspace{-0.1cm} \ =\ \hspace{-0.1cm} \sqrt {E} \cdot (1,\hspace{0.1cm} 5) \hspace{0.2cm} \Leftrightarrow \hspace{0.2cm} m_0 \hspace{0.05cm},$$
- $$ \boldsymbol{ s }_1 \hspace{-0.1cm} \ =\ \hspace{-0.1cm} \sqrt {E} \cdot (4, \hspace{0.1cm}1) \hspace{0.2cm} \Leftrightarrow \hspace{0.2cm} m_1 \hspace{0.05cm}.$$
The optimal decision boundary between the regions $I_0 ⇔ m_0$ and $I_1 ⇔ m_1$ is sought. The following assumptions are made:
- It applies to subtasks (1) to (3):
- $${\rm Pr}(m_0 ) = {\rm Pr}(m_1 ) = 0.5 \hspace{0.05cm}. $$
- For subtasks (4) and (5) should apply:
- $${\rm Pr}(m_0 ) = 0.817 \hspace{0.05cm},\hspace{0.2cm} {\rm Pr}(m_1 ) = 0.183\hspace{0.3cm} \Rightarrow \hspace{0.3cm} {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = 1.5 \hspace{0.05cm}.$$
For AWGN noise with variance $\sigma_n^2$, the decision boundary is the solution of the following vectorial equation with respect to the vector $\boldsymbol{ \rho } =(\rho_1, \rho_2)$:
- $$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 + 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = 2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$
In addition, two received values
- $$\boldsymbol{ A }= \sqrt {E} \cdot (1.5, \hspace{0.1cm}2)\hspace{0.05cm},$$
- $$\boldsymbol{ B }= \sqrt {E} \cdot (3, \hspace{0.1cm}3.5) $$
are drawn in the graphic. It must be checked whether these should be assigned to the regions $I_0$ $($and thus the message $m_0)$ or to $I_1$ $($message $m_1)$ given the corresponding boundary conditions.
Notes:
- The exercise belongs to the chapter "Approximation of the Error Probability".
- For numeric calculations, the energy $E = 1$ can be set for simplification.
Questions
Solution
- $$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 = 2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$
- With the given vector values, i.e. the numerical values
- $$|| \boldsymbol{ s }_1||^2 = 4^2 + 1^2 = 17\hspace{0.05cm}, \hspace{0.2cm} || \boldsymbol{ s }_0||^2 = 1^2 + 5^2 = 26\hspace{0.05cm}, \hspace{0.2cm} \boldsymbol{ s }_1 - \boldsymbol{ s }_0 = (3,\hspace{0.1cm}-4) \hspace{0.05cm}$$
- one obtains the following equation for the decision boundaries:
- $$3 \cdot \rho_1 - 4 \cdot \rho_2 = ({17-26})/{2} = - {9}/{2} \hspace{0.3cm}\Rightarrow \hspace{0.3cm}\rho_2 = 3/4 \cdot \rho_1 + 9/8 \hspace{0.05cm}.$$
- The decision line lies in the middle between $s_0$ and $s_1$ and is rotated by $90^\circ$ compared to the connecting line between the two symbols. It goes through the point $(2.5, \ \, 3)$. So the first solution is correct.
- Solution 2, on the other hand, describes the connecting line itself and $\rho_2 = 3$ is a horizontal line.
(2) The decision region $I_1$ should of course contain the point $s_1$ ⇒ region below the decision line.
- Point $A = (1.5, \ \, 2)$ belongs to this decision region, as shown in the graphic.
- This can be shown mathematically, since the decision line goes through the point $(1.5, \ \, 2.25)$, for example, and thus $(1.5, \ \, 2)$ lies below the decision line.
- So, solution 2 is correct.
(3) The decision line also goes through the point $(3, \ \, 3.375)$.
- $B = (3, \ \, 3.5)$ lies above and therefore belongs to the decision region $I_0$ according to solution 1.
(4) According to the equation in the information section and the calculations for subtask (1), the following now applies:
- $$|| \boldsymbol{ s }_1||^2 - || \boldsymbol{ s }_0||^2 + 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = 2 \cdot \boldsymbol{ \rho }^{\rm T} \cdot (\boldsymbol{ s }_1 - \boldsymbol{ s }_0)\hspace{0.05cm}.$$
- With $|| \boldsymbol{ s }_1||^2 = 17$, $|| \boldsymbol{ s }_0||^2 = 26$, $ \boldsymbol{ s }_1 \, –\boldsymbol{ s }_0 = (3, \ \, –4)$ we obtain:
- $$\rho_2 = 3/4 \cdot \rho_1 + 9/8 - K /8 \hspace{0.05cm}.$$
- The following abbreviation was used here:
- $$K = 2 \cdot \sigma_n^2 \cdot {\rm ln} \hspace{0.15cm} \frac{{\rm Pr}( m_0)}{{\rm Pr}( m_1)} = 2 \cdot 1^2 \cdot 1.5 = 3 \hspace{0.05cm}.$$
- From this it follows:
- $$\rho_2 = 3/4 \cdot \rho_1 + 9/8 - 3 /8 = 3/4 \cdot \rho_1 + 3/4 \hspace{0.05cm}.$$
- The decision line is shifted down by $3/8$ $($black curve, labeled "$K = 3$" in the graphic$)$. So, solution 2 is correct.
- The first equation describes the optimal decision line for equally probable symbols $(K = 0$, dashed gray$)$.
- The third equation is valid for $K = \, –3$. This results with $\sigma_n^2 = 1$ for the symbol probabilities ${\rm Pr}(m_1) \approx 0.817$ and ${\rm Pr}(m_0) \approx 0.138$ (green curve).
- The violet straight line results with $K = 9$, i.e. with the same probabilities as for the black curve, but now with the variance $\sigma_n^2 = 3$.
(5) The graphic above already shows that both $A$ and $B$ now belong to the decision region $I_0$. Solutions 1 and 3 are correct.