Exercise 4.4: Extrinsic L-values at SPC
We consider again the "single parity–check code". In such a ${\rm SPC} \ (n, \, n-1, \, 2)$ the $n$ bits of a code word $\underline{x}$ come from the $k = n -1$ bits from the source sequence $\underline{u}$ and only a single check bit $p$ is added, such that the number of "ones" in the code word $\underline{x}$ is even:
- $$\underline{x} = \big ( \hspace{0.03cm}x_1, \hspace{0.03cm} x_2, \hspace{0.05cm} \text{...} \hspace{0.05cm} , x_{n-1}, \hspace{0.03cm} x_n \hspace{0.03cm} \big ) = \big ( \hspace{0.03cm}u_1, \hspace{0.03cm} u_2, \hspace{0.05cm} \text{...} \hspace{0.05cm} , u_{k}, \hspace{0.03cm} p \hspace{0.03cm} \big )\hspace{0.03cm}. $$
The extrinsic information about the $i$th code bit is formed over all other bits $(j ≠ i)$. Therefore we write for the code word shorter by one bit:
- $$\underline{x}^{(-i)} = \big ( \hspace{0.03cm}x_1, \hspace{0.05cm} \text{...} \hspace{0.05cm} , \hspace{0.03cm} x_{i-1}, \hspace{0.43cm} x_{i+1}, \hspace{0.05cm} \text{...} \hspace{0.05cm} , x_{n} \hspace{0.03cm} \big )\hspace{0.03cm}. $$
The extrinsic L–value over the $i$th code symbol reads with the "Hamming weight" $w_{\rm H}$ of the truncated sequence $\underline{x}^{(-i)}$:
- $$L_{\rm E}(i) = \frac{{\rm Pr} \left [w_{\rm H}(\underline{x}^{(-i)})\hspace{0.15cm}{\rm is \hspace{0.15cm} even} \hspace{0.05cm} | \hspace{0.05cm}\underline{y} \hspace{0.05cm}\right ]}{{\rm Pr} \left [w_{\rm H}(\underline{x}^{(-i)})\hspace{0.15cm}{\rm is \hspace{0.15cm} odd} \hspace{0.05cm} | \hspace{0.05cm}\underline{y} \hspace{0.05cm}\right ]} \hspace{0.05cm}.$$
- If the probability in the numerator is greater than that in the denominator, then $L_{\rm E}(i) > 0$ and thus the a-posteriori L–value $L_{\rm APP}(i) = L_{\rm A}(i) + L_{\rm E}(i)$ magnified, that is tends to be affected in the direction of the symbol $x_i = 0$.
- If $L_{\rm E}(i) < 0$ then there is much to be said for $x_i = 1$ from the point of view of the other symbols $(j ≠ i)$.
Only the $\text{SPC (4, 3, 4)}$ is treated, where for the probabilities $p_i = {\rm Pr}(x_i = 1)$ holds:
- $$p_1 = 0.2 \hspace{0.05cm}, \hspace{0.3cm} p_2 = 0.9 \hspace{0.05cm}, \hspace{0.3cm} p_3 = 0.3 \hspace{0.05cm}, \hspace{0.3cm} p_4 = 0.6 \hspace{0.05cm}.$$
From this the a-priori log likelihood ratios result to:
- $$L_{\rm A}(i) = {\rm ln} \hspace{0.1cm} \left [ \frac{{\rm Pr}(x_i = 0)}{{\rm Pr}(x_i = 1)} \right ] = {\rm ln} \hspace{0.1cm} \left [ \frac{1-p_i}{p_i} \right ] \hspace{0.05cm}.$$
Hints:
- This exercise belongs to the chapter "Soft–in Soft–out Decoder".
- Reference is made in particular to the section "Calculation of the extrinsic log likelihood ratios".
- In the table are given for $p_i = 0$ to $p_i = 1$ with step size $0.1$ $($column 1$)$:
- In column 2: the probability $q_i = {\rm Pr}(x_i = 0) = 1 - p_i$,
- in column 3: the values for $1 - 2p_i$,
- in column 4: the a-priori log likelihood ratios $L_i = \ln {\big [(1 - p_i)/p_ i \big ]} = L_{\rm A}(i)$.
- The "hyperbolic tangent" $(\tanh)$ of $L_i/2$ is identical to $1-2p_i$ ⇒ column 3.
- In $\text{Exercise 4.4Z}$ it is shown that for the extrinsic L–value can also be written:
- $$L_{\rm E}(i) = {\rm ln} \hspace{0.2cm} \frac{1 + \pi}{1 - \pi}\hspace{0.05cm}, \hspace{0.3cm} {\rm mit} \hspace{0.3cm} \pi = \prod\limits_{j \ne i}^{n} \hspace{0.25cm}(1-2p_j) \hspace{0.05cm}.$$
Questions
Solution
- $$L_{\rm A}(i = 1) \hspace{-0.15cm} \ = \ \hspace{-0.15cm} {\rm ln} \hspace{0.1cm} \left [ \frac{1-p_1}{p_1} \right ] = {\rm ln} \hspace{0.1cm} 4 \hspace{0.15cm}\underline{= +1.386} \hspace{0.05cm},$$
- $$L_{\rm A}(i = 2) \hspace{-0.15cm} \ = \ \hspace{-0.15cm} {\rm ln} \hspace{0.1cm} \left [ \frac{1-p_2}{p_2} \right ] = {\rm ln} \hspace{0.1cm} 1/9 \hspace{0.15cm}\underline{= -2.197} \hspace{0.05cm}.$$
- The values can be read from the fourth column of the table attached to the information page.
(2) To calculate the extrinsic L–value over the $i$th bit, only the information about the other three bits $(j ≠ i)$ may be used. With the given equation holds:
- $$L_{\rm E}(i = 1) = {\rm ln} \hspace{0.2cm} \frac{1 + \prod\limits_{j \ne 1} \hspace{0.25cm}(1-2p_j)}{1 - \prod\limits_{j \ne 1} \hspace{0.25cm}(1-2p_j)} \hspace{0.05cm}.$$
- For the product, we obtain according to the third column of the $\rm table$:
- $$\prod\limits_{j =2, \hspace{0.05cm}3,\hspace{0.05cm} 4} \hspace{0.05cm}(1-2p_j) = (-0.8) \cdot (+0.4) \cdot (-0.2) = 0.064 \hspace{0.05cm}\hspace{0.3cm} \Rightarrow \hspace{0.3cm}L_{\rm E}(i = 1) = {\rm ln} \hspace{0.2cm} \frac{1 + 0.064}{1 - 0.064} = {\rm ln} \hspace{0.1cm} (1.137)\hspace{0.15cm}\underline{= +0.128} \hspace{0.05cm}.$$
- In terms of bit 2, one obtains accordingly:
- $$\prod\limits_{j =1, \hspace{0.05cm}3,\hspace{0.05cm} 4} \hspace{0.05cm}(1-2p_j) = (+0.6) \cdot (+0.4) \cdot (-0.2) = -0.048 \hspace{0.05cm}\hspace{0.3cm} \Rightarrow \hspace{0.3cm}L_{\rm E}(i = 2) = {\rm ln} \hspace{0.2cm} \frac{1 -0.048}{1 +0.048} = {\rm ln} \hspace{0.1cm} (0.908)\hspace{0.15cm}\underline{= -0.096} \hspace{0.05cm}.$$
(3) For the a-priori L– value holds:
- $$L_j = L_{\rm A}(j) = {\rm ln} \hspace{0.1cm} \left [ \frac{{\rm Pr}(x_j = 0)}{{\rm Pr}(x_j = 1)} \right ] = {\rm ln} \hspace{0.1cm} \left [ \frac{1-p_j}{p_j} \right ]\hspace{0.3cm} \Rightarrow \hspace{0.3cm} 1-p_j = p_j \cdot {\rm e}^{L_j} \hspace{0.3cm} \Rightarrow \hspace{0.3cm} p_j = \frac{1}{1+{\rm e}^{L_j} } \hspace{0.05cm} .$$
- Thus also applies:
- $$1- 2 \cdot p_j = 1 - \frac{2}{1+{\rm e}^{L_j} } = \frac{1+{\rm e}^{L_j}-2}{1+{\rm e}^{L_j} } = \frac{{\rm e}^{L_j}-1}{{\rm e}^{L_j} +1}\hspace{0.05cm} .$$
- Multiplying the numerator and denominator by ${\rm e}^{-L_j/2}$, we get:
- $$1- 2 \cdot p_j = \frac{{\rm e}^{L_j/2}-{\rm e}^{-L_j/2}}{{\rm e}^{L_j/2}+{\rm e}^{-L_j/2}}={\rm tanh} (L_j/2) \hspace{0.05cm} .$$
- Thus all proposed solutions are correct.
- The function "hyperbolic tangent" can be found, for example, in tabular form in formula collections or in the last column of the table given in front.
(4) We first calculate $L_{\rm E}(i = 3)$ in the same way as in subtask (2):
- $$\prod\limits_{j =1, \hspace{0.05cm}2,\hspace{0.05cm} 4} \hspace{0.05cm}(1-2p_j) = (+0.6) \cdot (-0.8) \cdot (-0.2) = +0.096 \hspace{0.05cm}\hspace{0.3cm} \Rightarrow \hspace{0.3cm}L_{\rm E}(i = 3) = {\rm ln} \hspace{0.2cm} \frac{1 +0.096}{1 -0.096} = {\rm ln} \hspace{0.1cm} (1.212)\hspace{0.15cm}\underline{= +0.193} \hspace{0.05cm}.$$
- We calculate the extrinsic L–value with respect to the last bit according to the equation
- $$L_{\rm E}(i = 4) = {\rm ln} \hspace{0.2cm} \frac{1 + \pi}{1 - \pi}\hspace{0.05cm}, \hspace{0.3cm} {\rm with} \hspace{0.3cm} \pi = {\rm tanh}(L_1/2) \cdot {\rm tanh}(L_2/2) \cdot {\rm tanh}(L_3/2) \hspace{0.05cm}.$$
- This results in accordance with the above $\rm table$:
- $$p_1 = 0.2 \hspace{0.2cm}\Rightarrow \hspace{0.2cm} L_1 = +1.386 \hspace{0.2cm}\Rightarrow \hspace{0.2cm} L_1/2 = +0.693 \hspace{0.2cm} \Rightarrow \hspace{0.2cm} {\rm tanh}(L_1/2) = \frac{{\rm e}^{+0.693}-{\rm e}^{-0.693}}{{\rm e}^{+0.693}+{\rm e}^{-0.693}} = 0.6 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}{\rm identisch \hspace{0.15cm}mit\hspace{0.15cm} }1-2\cdot p_1\hspace{0.05cm},$$
- $$p_2 = 0.9 \hspace{0.2cm}\Rightarrow \hspace{0.2cm} L_2 = -2.197 \hspace{0.2cm}\Rightarrow \hspace{0.2cm} L_2/2 = -1.099\hspace{0.2cm} \Rightarrow \hspace{0.2cm} {\rm tanh}(L_2/2) = \frac{{\rm e}^{-1.099}-{\rm e}^{+1.099}}{{\rm e}^{-1.099}+{\rm e}^{+1.099}} = -0.8 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}{\rm identisch \hspace{0.15cm}mit\hspace{0.15cm} }1-2\cdot p_2\hspace{0.05cm},$$
- $$p_3 = 0.3 \hspace{0.2cm}\Rightarrow \hspace{0.2cm} L_3 = 0.847 \hspace{0.2cm}\Rightarrow \hspace{0.2cm} L_3/2 = +0.419 \hspace{0.2cm} \Rightarrow \hspace{0.2cm} {\rm tanh}(L_3/2) = \frac{{\rm e}^{+0.419}-{\rm e}^{-0.419}}{{\rm e}^{+0.419}+{\rm e}^{-0.419}} = 0.4 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}{\rm identisch \hspace{0.15cm}mit\hspace{0.15cm} }1-2\cdot p_3\hspace{0.05cm}.$$
- The final result is thus:
- $$\pi = (+0.6) \cdot (-0.8) \cdot (+0.4) = -0.192 \hspace{0.3cm}\Rightarrow \hspace{0.3cm} L_{\rm E}(i = 4) = {\rm ln} \hspace{0.2cm} \frac{1 -0.192}{1 +0.192}\hspace{0.15cm}\underline{= -0.389} \hspace{0.05cm}.$$