5,517 research outputs found
Mathematical Programming Decoding of Binary Linear Codes: Theory and Algorithms
Mathematical programming is a branch of applied mathematics and has recently
been used to derive new decoding approaches, challenging established but often
heuristic algorithms based on iterative message passing. Concepts from
mathematical programming used in the context of decoding include linear,
integer, and nonlinear programming, network flows, notions of duality as well
as matroid and polyhedral theory. This survey article reviews and categorizes
decoding methods based on mathematical programming approaches for binary linear
codes over binary-input memoryless symmetric channels.Comment: 17 pages, submitted to the IEEE Transactions on Information Theory.
Published July 201
Quantum Graphical Models and Belief Propagation
Belief Propagation algorithms acting on Graphical Models of classical
probability distributions, such as Markov Networks, Factor Graphs and Bayesian
Networks, are amongst the most powerful known methods for deriving
probabilistic inferences amongst large numbers of random variables. This paper
presents a generalization of these concepts and methods to the quantum case,
based on the idea that quantum theory can be thought of as a noncommutative,
operator-valued, generalization of classical probability theory. Some novel
characterizations of quantum conditional independence are derived, and
definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and
Bayesian Networks are proposed. The structure of Quantum Markov Networks is
investigated and some partial characterization results are obtained, along the
lines of the Hammersely-Clifford theorem. A Quantum Belief Propagation
algorithm is presented and is shown to converge on 1-Bifactor Networks and
Markov Networks when the underlying graph is a tree. The use of Quantum Belief
Propagation as a heuristic algorithm in cases where it is not known to converge
is discussed. Applications to decoding quantum error correcting codes and to
the simulation of many-body quantum systems are described.Comment: 58 pages, 9 figure
Complexity Analysis of Reed-Solomon Decoding over GF(2^m) Without Using Syndromes
For the majority of the applications of Reed-Solomon (RS) codes, hard
decision decoding is based on syndromes. Recently, there has been renewed
interest in decoding RS codes without using syndromes. In this paper, we
investigate the complexity of syndromeless decoding for RS codes, and compare
it to that of syndrome-based decoding. Aiming to provide guidelines to
practical applications, our complexity analysis differs in several aspects from
existing asymptotic complexity analysis, which is typically based on
multiplicative fast Fourier transform (FFT) techniques and is usually in big O
notation. First, we focus on RS codes over characteristic-2 fields, over which
some multiplicative FFT techniques are not applicable. Secondly, due to
moderate block lengths of RS codes in practice, our analysis is complete since
all terms in the complexities are accounted for. Finally, in addition to fast
implementation using additive FFT techniques, we also consider direct
implementation, which is still relevant for RS codes with moderate lengths.
Comparing the complexities of both syndromeless and syndrome-based decoding
algorithms based on direct and fast implementations, we show that syndromeless
decoding algorithms have higher complexities than syndrome-based ones for high
rate RS codes regardless of the implementation. Both errors-only and
errors-and-erasures decoding are considered in this paper. We also derive
tighter bounds on the complexities of fast polynomial multiplications based on
Cantor's approach and the fast extended Euclidean algorithm.Comment: 11 pages, submitted to EURASIP Journal on Wireless Communications and
Networkin
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