256 research outputs found
Quantifying Equivocation for Finite Blocklength Wiretap Codes
This paper presents a new technique for providing the analysis and comparison
of wiretap codes in the small blocklength regime over the binary erasure
wiretap channel. A major result is the development of Monte Carlo strategies
for quantifying a code's equivocation, which mirrors techniques used to analyze
normal error correcting codes. For this paper, we limit our analysis to
coset-based wiretap codes, and make several comparisons of different code
families at small and medium blocklengths. Our results indicate that there are
security advantages to using specific codes when using small to medium
blocklengths.Comment: Submitted to ICC 201
Low-Complexity Joint Channel Estimation and List Decoding of Short Codes
A pilot-assisted transmission (PAT) scheme is proposed for short
blocklengths, where the pilots are used only to derive an initial channel
estimate for the list construction step. The final decision of the message is
obtained by applying a non-coherent decoding metric to the codewords composing
the list. This allows one to use very few pilots, thus reducing the channel
estimation overhead. The method is applied to an ordered statistics decoder for
communication over a Rayleigh block-fading channel. Gains of up to dB as
compared to traditional PAT schemes are demonstrated for short codes with QPSK
signaling. The approach can be generalized to other list decoders, e.g., to
list decoding of polar codes.Comment: Accepted at the 12th International ITG Conference on Systems,
Communications and Coding (SCC 2019), Rostock, German
Ultra-Sparse Non-Binary LDPC Codes for Probabilistic Amplitude Shaping
This work shows how non-binary low-density parity-check codes over GF()
can be combined with probabilistic amplitude shaping (PAS) (B\"ocherer, et al.,
2015), which combines forward-error correction with non-uniform signaling for
power-efficient communication. Ultra-sparse low-density parity-check codes over
GF(64) and GF(256) gain 0.6 dB in power efficiency over state-of-the-art binary
LDPC codes at a spectral efficiency of 1.5 bits per channel use and a
blocklength of 576 bits. The simulation results are compared to finite length
coding bounds and complemented by density evolution analysis.Comment: Accepted for Globecom 201
Sparse Graph Codes for Quantum Error-Correction
We present sparse graph codes appropriate for use in quantum
error-correction. Quantum error-correcting codes based on sparse graphs are of
interest for three reasons. First, the best codes currently known for classical
channels are based on sparse graphs. Second, sparse graph codes keep the number
of quantum interactions associated with the quantum error correction process
small: a constant number per quantum bit, independent of the blocklength.
Third, sparse graph codes often offer great flexibility with respect to
blocklength and rate. We believe some of the codes we present are unsurpassed
by previously published quantum error-correcting codes.Comment: Version 7.3e: 42 pages. Extended version, Feb 2004. A shortened
version was resubmitted to IEEE Transactions on Information Theory Jan 20,
200
Locally Testable Codes and Cayley Graphs
We give two new characterizations of (\F_2-linear) locally testable
error-correcting codes in terms of Cayley graphs over \F_2^h:
\begin{enumerate} \item A locally testable code is equivalent to a Cayley
graph over \F_2^h whose set of generators is significantly larger than
and has no short linear dependencies, but yields a shortest-path metric that
embeds into with constant distortion. This extends and gives a
converse to a result of Khot and Naor (2006), which showed that codes with
large dual distance imply Cayley graphs that have no low-distortion embeddings
into .
\item A locally testable code is equivalent to a Cayley graph over \F_2^h
that has significantly more than eigenvalues near 1, which have no short
linear dependencies among them and which "explain" all of the large
eigenvalues. This extends and gives a converse to a recent construction of
Barak et al. (2012), which showed that locally testable codes imply Cayley
graphs that are small-set expanders but have many large eigenvalues.
\end{enumerate}Comment: 22 page
Reed-Muller codes for random erasures and errors
This paper studies the parameters for which Reed-Muller (RM) codes over
can correct random erasures and random errors with high probability,
and in particular when can they achieve capacity for these two classical
channels. Necessarily, the paper also studies properties of evaluations of
multi-variate polynomials on random sets of inputs.
For erasures, we prove that RM codes achieve capacity both for very high rate
and very low rate regimes. For errors, we prove that RM codes achieve capacity
for very low rate regimes, and for very high rates, we show that they can
uniquely decode at about square root of the number of errors at capacity.
The proofs of these four results are based on different techniques, which we
find interesting in their own right. In particular, we study the following
questions about , the matrix whose rows are truth tables of all
monomials of degree in variables. What is the most (resp. least)
number of random columns in that define a submatrix having full column
rank (resp. full row rank) with high probability? We obtain tight bounds for
very small (resp. very large) degrees , which we use to show that RM codes
achieve capacity for erasures in these regimes.
Our decoding from random errors follows from the following novel reduction.
For every linear code of sufficiently high rate we construct a new code
, also of very high rate, such that for every subset of coordinates, if
can recover from erasures in , then can recover from errors in .
Specializing this to RM codes and using our results for erasures imply our
result on unique decoding of RM codes at high rate.
Finally, two of our capacity achieving results require tight bounds on the
weight distribution of RM codes. We obtain such bounds extending the recent
\cite{KLP} bounds from constant degree to linear degree polynomials
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