Berlekamp-Massey algorithm demo program

This program is a Berlekamp-Massey algorithm demo program.

Preparation

Install sage. This program is written in sage. Sage is a programming language for mathematicians. Sage is python-based language and has many extensive features for algebra, algebraic geometry and number theory.

Downloading the demo program

Clone demo program's repository.

git clone https://github.com/xenocaliver/BMDecoder.git ./

Run the demo program

Run demo program as follows. Regarding BCH code's demo program, run BCHDecoder.py.

sage BCHDecoder.py

Regarding Reed-Solomon code's demo program, run RSDecoder.py.

sage RSDecoder.py

Implimentation details

Cyclic codes

Before discussing Berlekamp-Massey algorithm, we must discuss cyclic codes. We begin crush course for cyclic codes. For persons who want to learn cyclic codes deeply, I recommend coding theory text book e.g. Error Correction Coding: Mathematical Methods and Algorithms. I assume that readers know basic algebra.

Let $ \mathbb{F}{q} $ be a finite field with characteristic $ q $. In coding theory, $ q=2^{M} $ and $ M $ is a natural number. Let $ k $ be a natural number called "dimension". $ m{i}\in\mathbb{F}{q}(i=0,\ldots,k-1) $ and we call $ \boldsymbol{m} = (m{0},\ldots,m_{k-1}) $ a message vector. We express the message as a polynomial

$$ m(x)=m_{0}+m_{1}x+\cdots+m_{k-1}x^{k-1} $$

and we call $m(x)$ a message polynomial. Let $n$ be a integer "code length" such as $n>k$. We multiply $m(x)$ by $x^{n-k}$ and divide it by generator polynomial $g(x)$ and we have

$$ x^{n-k}m(x)=q(x)g(x)+r(x). $$

$q(x)$ is a quotient polynomial and $r(x)$ is a remainder polynomial. And $c(x)$ be a code polynomial and it is defined as follows:

$$ c(x)=x^{n-k}m(x)-r(x) = q(x)g(x).\tag{1} $$

A transmitter sends $c(x)$ and a receiver receives $y(x)$. One can check whether $y(x)$ has errors or not by means of dividing $y(x)$ by $g(x)$. If remainder $r(x)$ equals to $0$, $y(x) = c(x)$ in other words, $y(x)$ has no errors. On the other hand, if $r(x)\neq 0$, $y(x)$ has some errors.

BCH Codes

Let $p$ be a prime number and $m$ be a natural number. And let $q=p^{m}$ and $\mathbb{F}_{q}$ be a finite field over $q=p^{m}$. Let $\alpha$ be a primitive $n$-th root of unity. A BCH code whose length equals to $n$ and which can correct at least $t$ errors is defined as follows:

• Find $\mathbb{F}_{p^{m}}$ which as $n$ primitive $n$ - root of unity $\alpha$.
• Select Non-negative integer $b$.
• Write down $2t$ consecutive powers of $\alpha$:

$$ \alpha^{b},\alpha^{b+1},\ldots,\alpha^{b+2t-1}. $$

• Determine minimal polynomial with respect to $\mathbb{F}_{p}$ of each of powers of $\alpha$.
• generator polynomial $g(x)$ is Least Common Multiplier of above minimal polynomials.

If $b=1$ on construction procedure, the BCH code is said to be narrow. If $ n=p^{m}-1 $, the BCH code is called primitive.

Reed-Solomon Codes

Reed-Solomon codes is $ q $-ary BCH codes whose length equals to $ p^{m} - 1 $.

In $\mathbb{F}_{q}$, the minimal polynomials for any root $ \alpha $ is simply $ (x-\alpha) $. Therefore, generator polynomial of Reed-Solomon codes is

$$ g(x) = (x-\alpha^{b})(x-\alpha^{b+1})\cdots(x-\alpha^{b+2t-1}). $$

Decoding of BCH codes and Reed-Solomon codes

In (1), if $r(x)\neq 0$ then received word $y(x)$ has some errors. As per construction of generator polynomials of BCH codes and Reed-Solomon codes,

$$ g(\alpha)=g(\alpha^{2})=\cdots=g(\alpha^{2t})=0. \tag{2} $$

Let $e(x)$ be a error polynomial i.e.

$$ e(x)=\sum_{i=0}^{n-1}e_{i}x^{i} $$

where $e_{i}$ is a coefficient which represents error value and $i$ represents error posistion in the code word. For example, $e_{i}$ is ${0,1}$ in binary codes. $e_{i}$ is $\mathbb{F}_{q}$ value in non-binary codes. Thus, received word polynomial is written as follows:

$$ y(x)=c(x)+e(x). $$

Because $c(x)=q(x)g(x)$ and $(2)$,

$$ y(\alpha^{i})=e(\alpha^{i}),,,(i=0,\ldots,2t). $$

Then, we define syndromes as follows:

$$ S_{i} = e(\alpha^{i})=\sum_{j=0}^{n-1}e_{j}(\alpha^{i})^{j}. $$

Let $\nu$ be total number of errors and $k_{1},\ldots,k_{\nu}$ be error positions. Then, we have

$$ S_{i}=\sum_{j=1}^{\nu}e_{k_{j}}(\alpha^{i})^{k_{j}}=\sum_{j=1}^{\nu}e_{k_{j}}(\alpha^{k_{j}})^{i}. $$

Let $X_{j}=\alpha^{k_{j}}$, then we can re-write the syndromes as follows:

$$ S_{i}=\sum_{j=1}^{\nu}e_{k_{j}}(X_{j})^{i}. \tag{3} $$

Consecutive $2t$ syndromes are shown as follows:

$$ \begin{aligned} S_{1}&=e_{k_{1}}X_{1}+e_{k_{2}}X_{2}+\cdots+e_{k_{\nu}}X_{\nu}\\ S_{2}&=e_{k_{1}}X_{1}^{2}+e_{k_{2}}X_{2}^{2}+\cdots+e_{k_{\nu}}X_{\nu}^{2}\\ &\vdots \\ S_{2t}&=e_{k_{1}}X_{1}^{2t}+e_{k_{2}}X_{2}^{2t}+\cdots e_{k_{\nu}}X_{\nu}^{2t}. \end{aligned} $$

We construct a polynomial whose roots are inverse of $X_{j}$. The polynomial is shown as follows:

$$ \sigma(x)=\prod_{j=1}^{\nu}(1-X_{j}x)=\sigma_{\nu}x^{\nu}+\sigma_{\nu-1}x^{\nu-1}+\cdots+\sigma_{1}x+\sigma_{0}. \tag{4} $$

The polynomial is called error locator polynomial. By contruction, $\sigma_{0}=1$. Calculating $(4)$ directly, we get

$$ \begin{aligned} \sigma_{0}&=1\\ -\sigma_{1}&= X_{1}+\cdots+X_{\nu} = \sum_{k=1}^{\nu}X_{k}\\ \sigma_{2}&=X_{1}X_{2}+X_{1}X_{3}+\cdots+X_{1}X_{\nu}+\cdots+X_{\nu-1}X_{\nu}=\sum_{i<j}X_{i}X_{j}\\ -\sigma_{3}&=X_{1}X_{2}X_{3}+\cdots+X_{\nu-2}X_{\nu-1}X_{\nu}=\sum_{i<j<k}X_{i}X_{j}X_{k}\\ &\vdots\\ (-1)^{\nu}\sigma_{\nu}&=X_{1}X_{2}\cdots X_{\nu}. \end{aligned} $$

As per Newton's identities, $k$-th syndrome $S_{k}$ is given by

$$ -S_{k}=\sigma_{k-\nu}S_{k-\nu}+\sigma_{k-1}S_{k-\nu+1}+\cdots+\sigma_{1}S_{k-1}. $$

Here, we define two important polynomials. We define

$$ S(x)=S_{1}+S_{2}x+\cdots+S_{3}x^{2}+\cdots+S_{2t}x^{2t-1}=\sum_{j=0}^{2t-1}S_{j+1}x^{j}. $$

$S(x)$ is called syndrome polynomial. And error-evaluator polynomial $\mathit{\Omega}(x)$ is defined by

$$ \mathit{\Omega}(x)\equiv S(x)\sigma(x)\pmod{x^{2t}}. \tag{5} $$

Equation $(5)$ is called key equation. We are going to determine error locator poylnomial $\sigma(x)$ and locate error positions. After that we are going to determine error-evaluator polynomial and determine correct value of each symbol.

Berlekamp-Massey Algorithm

Berlekamp-Massey algorithm is an algorithm to determine error locator polynomial. We illustrate Berlekamp-Massey algorithm according to Chang. The authors slightliy changed original Berlekamp-Massey algorithm in order to design faster decoder circuitry. We implemented code according to Chang. The algorithm is shown as follows:

Initialization

$$ \begin{aligned} D^{(-1)}&=0\\ \delta&=1\\ \sigma^{(-1)}(x)&=\tau^{(-1)}(x)=1\\ \mathit{\Delta}^{(0)}&= S_{1} \end{aligned} $$

iteration

for $i=0$ to $2t-1$

$$ \begin{aligned} \sigma^{(i)}(x)&=\delta\sigma^{(i-1)}(x)+\mathit{\Delta}^{(i-1)}x\tau^{(i-1)}(x)\\ \mathit{\Delta}^{(i+1)}&=S_{i+2}\sigma_{0}^{(i)}+S_{i+1}\sigma_{1}^{(i)}+\cdots+S_{i-\nu_{i}+2}\sigma_{\nu_{i}}^{(i)} \end{aligned} $$

if $\mathit{\Delta}^{(i)}=0$ or $2D^{(i-1)}\geqq i+1$

$$ \begin{aligned} D^{(i)}&=D^{(i-1)}\\ \tau^{(i)}(x)&=x\tau^{(i-1)}(x) \end{aligned} $$

else

$$ \begin{aligned} D^{(i)}&= i + 1 - D^{(i-1)}\\ \delta&=\mathit{\Delta}^{(i)}\\ \tau^{(i)}(x)&=\sigma^{(i-1)}(x). \end{aligned} $$

After $2t-1$ iterations, error locator polynomial $\sigma(x)$ is constructed.

Chien Search

In order to determine error positions, we must find $\sigma(x)$'s roots. Therefore, for $i=1$ to $2t$, subsutitute $\alpha^{-i}$ into $\sigma(x)$ and judge if $\sigma(\alpha^{-i})=0$ or not. In order to accelerate searching speed, it is usual to implement several chien search circuits and prallelize search procedure.

Forney's formula

After finding error locations, we step forward to find correct value. Using Forney's formula, One can find correct value $e_{k_{j}}$ at location $X_{k_{j}}$:

$$ e_{k_{j}}=-\frac{\mathit{\Omega}(X^{-k_{j}})}{\sigma^{\prime}(X^{-k_{j}})} $$

where

$$ \sigma^{\prime}(x) = \frac{d\sigma(x)}{dx}. $$

As for BCH codes, it is sufficient to find error position because it is binary. Therefore, BCH decoders do not need Forney's formula.