# Exercise 1.4: Maximum Likelihood Decision

Maximum likelihood decoding model

We consider the digital transmission system according to the graph.  Considered are:

• a systematic  $(5, 2)$  block code  $\mathcal{C}$  with the code words
$$\underline{x}_{0} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} (0, 0, 0, 0, 0) \hspace{0.05cm},$$ $$\underline{x}_{1} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} (0, 1, 0, 1, 0) \hspace{0.05cm},$$ $$\underline{x}_{2} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} (1, 0, 1, 0, 1) \hspace{0.05cm},$$ $$\underline{x}_{3} \hspace{-0.1cm} \ = \ \hspace{-0.1cm} (1, 1, 1, 1, 1) \hspace{0.05cm};$$
• a digital (binary) channel model that changes the vector  $\underline{x} \in {\rm GF} (2^{5})$  over into the vector  $\underline{y} \in {\rm GF} (2^{5})$,
• Maximum likelihood decoder  (short:   ML decoder)  with the decision rule
$$\underline{z} = {\rm arg} \max_{\underline{x}_{\hspace{0.03cm}i} \hspace{0.05cm} \in \hspace{0.05cm} \mathcal{C}} \hspace{0.1cm} {\rm Pr}( \underline{x}_{\hspace{0.03cm}i} \hspace{0.05cm}|\hspace{0.05cm} \underline{y} ) = {\rm arg} \min_{\underline{x}_{\hspace{0.03cm}i} \hspace{0.05cm} \in \hspace{0.05cm} \mathcal{C}} \hspace{0.1cm} d_{\rm H}(\underline{y} \hspace{0.05cm}, \hspace{0.1cm}\underline{x}_{\hspace{0.03cm}i}).$$

Here,  $d_{\rm H} (\underline{y}, \ \underline{x_{i}})$  is the  Hamming distance  between the received word  $\underline{y}$  and the  (possibly)  sent code word  $\underline{x_{i}}$.

Note:  This exercise belongs to the chapter  "Channel Models and Decision Structures".

### Questions

1

Let  $\underline{y} = (1, 0, 0, 0, 1)$.  Which decisions satisfy the maximum likelihood criterion?

 $\underline{z} = \underline{x}_{0} = (0, 0, 0, 0, 0)$, $\underline{z} = \underline{x}_{1} = (0, 1, 0, 1, 0)$, $\underline{z} = \underline{x}_{2} = (1, 0, 1, 0, 1)$, $\underline{z} = \underline{x}_{3} = (1, 1, 1, 1, 1)$.

2

Let  $\underline{y} = (0, 0, 0, 1, 0)$.  Which decisions satisfy the maximum likelihood criterion?

 $\underline{z} = \underline{x}_{0} = (0, 0, 0, 0, 0)$, $\underline{z} = \underline{x}_{1} = (0, 1, 0, 1, 0)$, $\underline{z} = \underline{x}_{2} = (1, 0, 1, 0, 1)$, $\underline{z} = \underline{x}_{3} = (1, 1, 1, 1, 1)$.

3

What decision does the maximum likelihood decoder make for  $\underline{y} = (1, 0, 1, 1, 1)$ when it is told that the last two symbols are uncertain?

 $\underline{z} = \underline{x}_{0} = (0, 0, 0, 0, 0)$, $\underline{z} = \underline{x}_{1} = (0, 1, 0, 1, 0)$, $\underline{z} = \underline{x}_{2} = (1, 0, 1, 0, 1)$, $\underline{z} = \underline{x}_{3} = (1, 1, 1, 1, 1)$.

4

What information word  $v = (v_{1}, v_{2})$  does the decision lead to according to the last subtask?

 $v_{1} \ = \$ $v_{2} \ = \$

### Solution

#### Solution

(1)  Correct  answer 3   ⇒   $\underline{z} = \underline{x}_{2} = (1, 0, 1, 0, 1)$:

• The Hamming distances between the specific received word $\underline{y} = (1, 0, 0, 0, 1)$ and the four possible code words $\underline{x}_{i}$ are as follows:
$$d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_0) = 2\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_1) = 4\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_2) = 1\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_3) = 3\hspace{0.05cm}.$$
• A decision is made for the sequence with the smallest Hamming distance $d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_2) = 1$.

(2)  For  $\underline{y} = (0, 0, 0, 1, 0)$  the  answers 1 and 2  are correct,  as the following calculation shows:

$$d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_0) = 1\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_1) = 1\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_2) = 4\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_3) = 4\hspace{0.05cm}.$$

(3)  Correct answer 3   ⇒   $\underline{z} = \underline{x}_{2} = (1, 0, 1, 0, 1)$:

• According to the Hamming distance,  a decision in favor of  $x_{2}$  would be just as possible as for  $x_{3}$  if the vector  $\underline{y} = (1, 0, 1, 1, 1)$  is received:
$$d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_0) = 4\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_1) = 4\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_2) = 1\hspace{0.05cm}, \hspace{0.3cm} d_{\rm H}(\underline{y}, \hspace{0.05cm}\underline{x}_3) = 1\hspace{0.05cm}.$$
• But the received vector  $\underline{y}$  is different from  $x_{2}$  with respect to the fourth bit and from  $x_{3}$  in the second bit.
• Since the fourth bit is more uncertain than the second,  it will choose  $x_{2}$ .

(4)  Since this is a systematic code,  the decision for  $\underline{z} = (1, 0, 1, 0, 1)$  is equivalent to the decision

$$v_{1} \ \underline{ = 1}, \ v_{2} \ \underline{= 0}.$$
• It is not certain that  $\underline{u} = (1, 0)$  was actually sent.
• But the probability is highest for this given the received vector $\underline{y} = (1, 0, 1, 1, 1)$.