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\lecture{11}{October 16, 2001}{Madhu Sudan}{Rui Fan}
\section{Overview}
\begin{itemize}
\item Decoding RS codes.
\item Abstract decoding algorithm.
\end{itemize}
\section{Decoding Reed-Solomon codes}
So far we've seen how to encode messages with the RS code. Recall
that the RS encoding of a message $m = \langle m_0, \ldots, m_{k-1}
\rangle$ is $\langle p(\alpha_1), \ldots, p(\alpha_n) \rangle$. Here,
$m_i \in \mathbb{F}_q$, $k \leq n \leq q$, and $p(x) =
\sum_{i=0}^{k-1} m_i x^i$. The decoding problem for RS codes can be
stated as follows:
Suppose we are given distinct values $\alpha_1, \ldots, \alpha_n$,
$\alpha_i \in \mathbb{F}_q$. Let $r_1, \ldots, r_n$ be our received
vector, $r_i \in \mathbb{F}_q$. Let $k$ and $e$ be parameters, where
$e$ is $\geq$ the number of errors which occurred in the received
vector. Our goal is to find a polynomial $p$ of degree $\leq k$, such
that $p(\alpha_i) \neq r_i$ for at most $e$ values of $i$.
Berlekamp and Welch gave an algorithm in 1986 which finds a unique $p$
for the above problem if $p$ exists, and $e \leq (n-k)/2$.
Interestingly, finding the actual solution is the only way we know of to
prove that a solution exists.
\subsection{Error-locating polynomial}
The BK algorithm starts by defining an \emph{error-locating
polynomial}. This is a polynomial which is associated with the
inputs, but which we don't (yet) know how to find. Nevertheless, it
has some nice properties which eventually lead to a decoding
algorithm.
\begin{definition}
Let $e,k$ be some parameters. Let $\alpha_1, \ldots, \alpha_n$ and
$r_1, \ldots, r_n$ be such that there exists a polynomial $p$ of
degree $\leq k$ such that $p(\alpha_i) \neq r_i$ for $\leq e$ values
of $i$. $E(x)$ is an \emph{error-locating polynomial} for the above
inputs if we have
\begin{enumerate}
\item $E(\alpha_i) = 0$ if $p(\alpha_i) \neq r_i$.
\item $\deg(E) \leq n-k-1$, and $E \neq 0$.
\end{enumerate}
\end{definition}
Note that given $E$, we can compute $p$, by finding the $i$'s for
which $E(\alpha_i) = 0$, replacing $r_i$ by $?$ for those $i$'s, and
doing erasure decoding on the resulting vector of $r_i$'s. This works
because there are at most $n-k-1 < d-1$ $i$'s for which $E(\alpha_i) =
0$. We now list some properties of $E$.
\begin{enumerate}
\item $E$ is nonzero, and has small degree.
\item $E \cdot p$ is a small degree polynomial.
\item $\forall i: (r_i - p(\alpha_i)) E(\alpha_i) = 0$
\end{enumerate}
Property 3 holds because either $r_i = p(\alpha_i)$, or $r_i
\neq p(\alpha_i)$ but $E(\alpha_i) = 0$.
Define $N(x) = E(x) \cdot p(x)$. By property 3, $N(\alpha_i) =
E(\alpha_i) p(\alpha_i) = r_i E(\alpha_i)$, for all $i$. We're now
ready for the decoding algorithm.
\subsection{Berlekamp-Welch decoding algorithm}
Input: $\alpha_1, \ldots, \alpha_n, r_1, \ldots, r_n$, with
$\alpha_i, r_i \in \mathbb{F}_q$. $k$, and $e \leq (n-k)/2$.
Step 1. Find polynomials $N(x) \neq 0$, $E(x) \neq 0$ such that
\begin{enumerate}
\item $N(\alpha_i) = r_i E(\alpha_i)$ for all $i$.
\item $\deg(N) \leq e + k$.
\item $\deg(E) \leq e$.
\end{enumerate}
Step 2. Output $N(x) / E(x)$ if $E | N$. Otherwise output ``no such polynomial''.
\subsubsection{Correctness}
To prove this algorithm is correct, we need to prove the following 3
things:
\begin{enumerate}
\item There exists $N, E$ satisfying the conditions of step 1.
\item The algorithm can be performed efficiently.
\item The solution it outputs is unique.
\end{enumerate}
\emph{Proof of 1.} Define $E(x) = \prod_{i : p(\alpha_i) \neq
r_i} (x - \alpha_i)$. If there are $\leq e$ errors, which is the
only case for which the algorithm needs to work correctly, then
$\deg(E) \leq e$. Now define $N(x) = E(x) p(x)$. We saw earlier
that for such an $N$, $N(\alpha_i) = r_i E(\alpha_i)$ for all $i$.
\emph{Proof of 2.} We first note that we can efficiently compute the
polynomial division in step 2. Step 1, finding $N(\alpha_i) = r_i
E(\alpha_i)$ for all $i$, is solving a homogeneous linear system of
$n$ equations
$$\sum_{j=0}^{e+k} N_j \alpha_i^j = r_i \sum_{j=0}^e E_j
\alpha_i^j$$
in the unknowns $N_0, \ldots, N_{e+k}, E_0, \ldots,
E_{e}$.We already saw that a solution to this system exists, in the
proof of property 1. The straightforward way of solving this system
works in time $O(n^3)$. Welch and Berlekamp gave an iterative
algorithm which makes $t$ passes over the input, where $t$ is the
number of errors, and has a total running time of $O(t \cdot n)$.
The fastest known algorithm, based on the Fast Fourier Transform,
takes time $O(n \mathrm{polylog} n)$.
\emph{Proof of 3.} Assume that we have pairs $(N,E)$, and $(N', E')$ which
both satisfy the conditions of step 1. We'll show that $\frac{N}{E}
= \frac{N'}{E'}$, so that the solution output by the algorithm is
unique. We'll do this by cross-multiplying and showing that $N
\cdot E' = N' \cdot E$. We consider 2 cases.
Case 1. $r_i = 0$: Then $N(\alpha_i) = N'(\alpha_i) = 0$, so we're
done.
Case 2. $r_i \neq 0$. Then $N(\alpha_i) N'(\alpha_i) = r_i
E(\alpha_i) N'(\alpha_i) = N(\alpha_i) r_i E'(\alpha_i)$. Dividing
through by $r_i$, we get that $E(\alpha_i) N'(\alpha_i) = N(\alpha_i)
E'(\alpha_i) \forall i \in [n]$. But since $N \cdot E'$ and $N' \cdot
E$ have degree $\leq 2e + k$, while $n > 2e + k$, this implies that $N
\cdot E' = N' \cdot E$ as polynomials. Thus, $\frac{N}{E}
= \frac{N'}{E'}$, and the output of the algorithm is unique.
\section{Abstract decoding procedure}
It turns out that the BK algorithm represents a style of decoding
which is not unique to RS codes. We now describe some work by
Pellikaan, K\"otter and Duursma which abstracts the BK algorithm, so
that it can be used to decode other codes, such as algebraic-geometry
codes. First we give some definitions.
\begin{definition}
Let $u, v \in \mathbb{F}_q^n$, and $A, B \subseteq \mathbb{F}_q^n$.
Define $u \star v = (u_1 v_1, \ldots, u_n v_n)$, and define $A \star
B = \{ a \star b \ | \ a \in A, b \in B\}$.
\end{definition}
The idea of the abstract decoding procedure is that given an $e$-error
correcting code $C$ which we want to decode, we construct an
\emph{error-locator code} $E$, such that $E \star C$ is contained in
some ``nice'' linear code $N$. By ``nice'', we mean that $N$ has
large distance. Specifically, we want codes $E$ and $N$ to have the
following properties:
\begin{enumerate}
\item $\dim(E) > e$.
\item $E \star C \subseteq N$.
\item dist$(N) > e$.
\item dist$(N) > n - $dist$(E)$.
\end{enumerate}
Then, given a received vector $(r_1, \ldots, r_n)$, such that there
exists a $c \in C$, $c = (c_1, \ldots, c_n)$ with $\Delta(r, c) \leq
e$, we want an algorithm to find $c$.
\subsection{Abstract decoding algorithm}
The abstract decoding algorithm parallels the concrete BK decoding
algorithm for RS codes. This algorithm works correctly if there are
$\leq e$ errors in $r$.
Input: A vector $r \in \mathbb{F}_q^n$ which we want to decode to a
codeword in $C$. Also, codes $E$ and $N$ with the properties
described in the last section.
Step 1. Find $a \in E$ and $b \in N$, $(a,b) \neq (0,0)$, such that $a
\star r = b$, and $a_i = 0$ if $r_i \neq c_i$.
Step 2. For any $i$ with $a_i = 0$, set $r_i = ?$. Then do erasure
decoding on the resulting vector $r$ to find $c$. If this does not
result in a codeword, output ``no such codeword''.
\subsubsection{Correctness}
As in the proof of the BK decoding algorithm, we need to argue 3
conditions.
\begin{enumerate}
\item A solution $(a,b)$ exists.
\item The algorithm is efficient.
\item The output $c$ is unique.
\end{enumerate}
\emph{Proof of 1.} We first show that there exists $a \in E$, $a \neq
0$, such that $a_i = 0$ if $r_i \neq c_i$. We assume that $\leq e$
errors occurred, since the algorithm only needs to work corectly in
this case. Then $a_i = 0$ for at most $e$ values of $i$. This places
at most $e$ linear constraints on $a$. But since $\dim(E) > e$, we
know that there is a nonzero vector $a \in E$ satisfying these
constraints. Now, define $b = a \star c$. Clearly $b \in N$. We
also have $b_i = a_i r_i$. This is because either we have $r_i =
c_i$, in which case $b_i = a_i c_i = a_i r_i$, or, we have $r_i \neq
c_i$, but then $a_i = 0$ and $a_i c_i = a_i r_i = 0$. Thus, there does
exist $(a,b) \neq (0,0)$ with the properties required by step 1.
\emph{Proof of 2.} To see that step 1 can be performed efficiently,
note that $E$ and $N$ are both linear spaces. Thus the requirement
that $a \in E$, $b \in N$ are linear constraints. Also, the
requirement that $a \star r = b$ is linear, since it just means $a_i
r_i = b_i$. Therefore, finding the required $a$ and $b$ is solving a
homogeneous linear system, which can be done efficiently. Also, step
2 can be done efficiently, since we saw that doing erasure decoding is
also solving a linear system. Thus, the algorithm is efficient.
\emph{Proof of 3.} We show that the output $c$ is unique with 2
lemmas.
\emph{Lemma 1} For any $(a,b)$ satisfying the conditions of step 1, we
hav $a \star c = b$.
\emph{Proof} We know that $a \star r = b$. Suppose $a \star c =
b'$. We show that $b = b'$. Since $b_i' = a_i c_i$, and $b_i = a_i
r_i$, we have $b_i \neq b_i'$ only if $c_i \neq r_i$. Since there are
$\leq e$ errors, there are at most $e$ such indices, so $\Delta(b,b')
\leq e$. But $b, b' \in N$, and dist$(N) > e$. Thus, $b = b'$.
\emph{Lemma 2} There is a unique $c$ such that $a \star c = b$.
\emph{Proof} Suppose that $a \star c' = b$. We need to show that $c'
= c$. We have $a \star c = a \star c'$. Also, since $a \in E$, $a_i
\neq 0$ for at least dist$(E)$ $i$'s. This means $c$ and $c'$ agree
on at least dist$(E)$ coordinates, and so $\Delta(c, c') < n -
$dist$(E)$. But $c, c' \in C$, and dist$(C) > n - $dist$(E)$. Thus,
$c = c'$.
Thus, the abstract decoding algorithm outputs a unique $c$. It's not
easy to find $E$ and $N$ which satisfy the requirements of the
abstract decoding algorithm. But such codes do exist, and next time,
we'll see an application of the abstract decoding algorithm.
\section{References}
Matt Lepinski's notes for lecture 11 of the 2001 version of this course.
\end{document}