\documentclass[10pt]{article}
\input{../preamble.tex}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{amssymb}
%\newtheorem{lemma}{Lemma}
%\newtheorem{define}{Definition}
%\newcommand{\Z}{{\mathbb{Z}}}
%\newcommand{\Z}{{\bf Z}}
%\usepackage{psfig}
\newcommand{\goesto}{\rightarrow}
\newcommand{\card}[1]{\lvert#1\rvert}
\newcommand{\FLIP}{\textsc{flip}}
%\newenvironment{proof}{}{}
\oddsidemargin=0.15in \evensidemargin=0.15in \topmargin=-.5in
\textheight=9in \textwidth=6.25in
\begin{document}
\lecture{17}{November 6, 2002}{Madhu Sudan}{Luis Rademacher}
Overview:
\begin{itemize}
\item Linear time encodable and decodable codes.
\item Shannon capacity with linear time algorithms.
\end{itemize}
\section{Introduction}
Graph-based codes were presented in the previous lecture. A linear
time decoding algorithm was presented for this codes, but encoding
may take quadratic time. Now, we are interested in efficient,
linear time encodable and decodable codes. Analogous to the idea
of sparse parity-check matrices, the encoding could be faster if
the generator matrix were sparse, but in this case it can't be a
good error correcting code: if the generator matrix has at most
$c$ ones per row (for small $c$), then changing $i^{\text{th}}$
bit of the message changes at most $c$ bits of its encoding.
Spielman's family of codes achieves linear time encoding and
decoding, and will be described in the following notes.
%It does not
%necessarily have a bad minimum distance.
\section{Error reducing code}
A sparse generator matrix cannot provide a good error correcting
code but can provide an error reducing code, that is, and encoding
and decoding procedure such that, under suitable hypothesis, can
reduce the number of errors (but not necessarily correct all of
them). In this case, it is possible to get linear time encoding
and decoding.
For a message length $k$, instead of specifying directly the
generator matrix, we will specify a bipartite graph $G$ with $k$
left nodes and $k/2$ right nodes. The associated generator matrix
will be:
\[
G := \begin{bmatrix}
I & A_g
\end{bmatrix},
\]
where $I$ is the $k \times k$ identity and $A_g$ is the $k \times
k/2$ adjacency matrix of the graph $G$. The first $k$ bits of the
encoding are the message itself, the last $k/2$ bits are the check
bits. This code will be denoted $R_k$.
Note that there is a fundamental difference between the meaning of
this graphs and the graphs in the previous lecture. In both cases
the left nodes are labelled with the bits of the message.
Previously, the right nodes represented constraints over the left
nodes, in this case the right nodes are labelled with check bits
that are computed from the parity of their (right) neighbors.
If the degree of the left nodes is at most $c$, then the minimum
distance is at most $c+1$ (a one-bit change in the message flips 1
message bit and at most $c$ check bits).
It is easy to see that this codes can be encoded in time $O(k)$.
If we use a slightly modified version of the \FLIP{} algorithm of
the previous lecture, we can achieve linear time error-reduction.
In terms of the previously defined \FLIP{} algorithm, an
unsatisfied right vertex in this case is one with a label
different to the parity of its neighbors.
%Analysis of error-reduction with \FLIP{}: We will show that if $ a
%\leq \epsilon k$ and $b \leq \epsilon k$ then the algorithm
%\FLIP{} stops at $(x',y)$ such that $\Delta (m,x') \leq b/2$.
The following lemma states in a precise way the error-reducing
capability of $R_k$:
\begin{lemma}
Assume that the bipartite graph $G$, with $k$ right nodes, is
$(c,c')$-regular and $(\gamma, \delta)$-expander, and suppose that
the original encoding, $(m,c)$, and the encoding with errors,
$(x,y)$, are $(a,b)$-close, that is $\Delta(m,x) \leq a$ and
$\Delta(c,y) \leq b$. Suppose also that $a(c+1)+b \leq \delta k$,
$\gamma \geq 7c/8$, and $c \geq 8$. Then \FLIP{} terminates and
outputs a word that differs from $m$ in at most $b/2$ bits.
Moreover, \FLIP{} takes time $O(k)$.
\end{lemma}
\begin{proof}
Denote $S=\{i: m_i \neq x_i\}$, $T =\{ i: c_i \neq y_i \}$. For
every iteration, the algorithm \FLIP{} decreases in at least one
the number of unsatisfied constraints, which is at most $ca+b$.
Then, the algorithm finishes with some word $x'$ such that
$\Delta(x',m) \leq \Delta(x,m) + ca + b \leq a + ca + b \leq
\delta k$. Denote $S'=\{i: m_i \neq x'_i\}$, then $\card{S'} \leq
\delta k$. Because $G$ is an expander, $\card{\Gamma_1 (S')} \geq
(2 \gamma - c) \card{S}$. Denote $U$ the set of unsatisfied right
nodes, at the end of the algorithm. In this situation, every left
vertex has at most $c/2$ unsatisfied right neighbors, thus
$\card{U} \leq \card{S'} c/2 $. On the other hand, $\Gamma_1(S') -
T \subseteq U$, then $\card{U} \leq (2\gamma - c) \card{S'} - b$.
The combination of both inequalities for $\card{U}$ implies $(2
\gamma - 3 c /2 ) \card{S'} \leq b$. This inequality and the
hypotheses give immediately $\card{S'} \leq b/2$.
\end{proof}
\section{Spielman's error-correcting code}
Denote $C_k$ the Spielman's code for messages of length $k$. This
code will be defined recursively in terms of error-reducing codes
and Spielman's codes for shorter messages. The intuitive idea is
to take into account the fact that the codes $R_k$ can be
error-correcting and efficient if we are able to protect the check
bits from errors. A key step in achieving this is to encode the
check bits with a Spielman's code of smaller message length.
More precisely, given a message of length $k$, its $C_k$-encoding
has length $4k$. The first $3k/2$ bits of the encoding are the
result of $R_k$ applied to the message, that is, the first $k$
bits of the encoding are the message itself (this bits are denoted
$M_k$) and the next $k/2$ bits are the check bits (denoted $A_k$).
This $k/2$ check bits are encoded with $C_{k/2}$ to provide the
next $3k/2$ check bits (denoted $B_k$). Finally, $(A_k,B_k)$, the
$2k$ bits that are the result of this $C_{k/2}$-encoding, are
encoded with $R_{2k}$ to provide $k$ additional check bits
(denoted $D_k$). The rate of $C_k$ is $1/4$.
For the encoding time of $C_k$, it is clear that $R_k$-encoding
takes time $O(k)$. Then, if $T_E(k)$ is the encoding time of
$C_k$, we have
\begin{align*}
T_E(k) \leq O(k) + T_E(\frac{k}{2}) + O(k).
\end{align*}
This implies that $T_E(k) = O(k)$.
A decoding algorithm for $C_k$ is:
\begin{enumerate}
\item Run \FLIP{} on $(A_k,B_k,D_k)$ (the last $3k$ bits) until it
stops to obtain $(A'_k,B'_k)$.
\item Recursively decode the result of the previous step
$(A'_k,B'_k)$ with $C_{k-1}$ to obtain $A''_k$.
\item Run \FLIP{} on $A''_k$, the result of the previous step, to
recover message.
\end{enumerate}
For the decoding time of $C_k$, steps 1 and 3 take linear time.
Then, if $T_D(k)$ is the decoding time of $C_k$, we have:
\begin{align*}
T_D(k) \leq O(k) + T_E(\frac{k}{2}) + O(k).
\end{align*}
This implies that $T_E(k) = O(k)$.
About the effectiveness of the decoding of $C_k$, suppose that we
allow at most $\epsilon k$ errors. After step 1, the number of
errors in $(A'_k,B'_k)$ is less that or equal to $\frac{\epsilon
k}{2}$. By inductive hypothesis, this implies that step 2 will
recover $B_k$ without errors. Because this are the check bits of
$R_k$ for the original message, this implies that algorithm
\FLIP{} will recover the message $M_k$ without errors in step
3.\footnote{In fact, the same argument and conclusion applies if
we allow $3 \epsilon k$ errors, distributed in the following way:
$\epsilon k$ errors in $M_k$, the message, $\epsilon k$ errors in
$(A_k,B_k)$, and $\epsilon k$ errors in $C_k$.}
\section{Modification to Spielman's codes to handle a tiny
fraction of errors with small overhead}
It is possible to modify Spielman's codes to have a rate better
than $1/4$, if we reduce the error-correction capability
appropriately, while we retain the linear encoding and decoding
property. The idea is to compute few check bits in the first step:
instead of $k/2$ check bits we consider $\alpha k$ bits, for small
$\alpha$, $0 < \alpha < 1$. Then we protect this check bits with
$C_{\alpha k}$, and add additional check bits as in the low-rate
case. This code has an $\epsilon > 0 $ fraction of errors
correcting capability, such that $\alpha \goesto 0$ as $\epsilon
\goesto 0$.
\section{The Random Errors Case}
Consider now the case of random errors that flip every bit
independently with probability $p$, that is, $BSC_p$. We will
construct a code based on Forney's idea, that is, composition, but
instead of considering the Reed-Solomon codes as the outer code we
will consider a high-rate Spielman's code (as described in the
previous section), that can correct an $\epsilon$ fraction of
(adversarial) errors and has rate $1+f(\epsilon)$, with
$f(\epsilon) \goesto 0$ as $\epsilon \goesto 0$. The inner code
takes blocks of length $c$ (independent of the message length $k$)
and gives encoded blocks of length $c'$, $c \approx (1 - H(p))c'$.
The encoded length is
\[
n:=\frac{k(1+f(\epsilon))}{1-H(p)}.
\]
The inner code is decoded by maximum likelihood decoding, that is,
in constant time. Because of a Chernoff-bound argument (or
Shannon's theorem), the probability of decoding error in a
particular block after inner decoding is very small. Thus, with
high probability the outer code is able to correct this errors.
The main conclusion of this argument is that we can achieve
correction of random errors up to Shannon's bound with linear time
encoding and decoding.
\nocite{*}
\bibliographystyle{plain}
\bibliography{spielman}
\end{document}