6,257 research outputs found
Stopping Set Distributions of Some Linear Codes
Stopping sets and stopping set distribution of an low-density parity-check
code are used to determine the performance of this code under iterative
decoding over a binary erasure channel (BEC). Let be a binary
linear code with parity-check matrix , where the rows of may be
dependent. A stopping set of with parity-check matrix is a subset
of column indices of such that the restriction of to does not
contain a row of weight one. The stopping set distribution
enumerates the number of stopping sets with size of with parity-check
matrix . Note that stopping sets and stopping set distribution are related
to the parity-check matrix of . Let be the parity-check matrix
of which is formed by all the non-zero codewords of its dual code
. A parity-check matrix is called BEC-optimal if
and has the smallest number of rows. On the
BEC, iterative decoder of with BEC-optimal parity-check matrix is an
optimal decoder with much lower decoding complexity than the exhaustive
decoder. In this paper, we study stopping sets, stopping set distributions and
BEC-optimal parity-check matrices of binary linear codes. Using finite geometry
in combinatorics, we obtain BEC-optimal parity-check matrices and then
determine the stopping set distributions for the Simplex codes, the Hamming
codes, the first order Reed-Muller codes and the extended Hamming codes.Comment: 33 pages, submitted to IEEE Trans. Inform. Theory, Feb. 201
On the similarities between generalized rank and Hamming weights and their applications to network coding
Rank weights and generalized rank weights have been proven to characterize
error and erasure correction, and information leakage in linear network coding,
in the same way as Hamming weights and generalized Hamming weights describe
classical error and erasure correction, and information leakage in wire-tap
channels of type II and code-based secret sharing. Although many similarities
between both cases have been established and proven in the literature, many
other known results in the Hamming case, such as bounds or characterizations of
weight-preserving maps, have not been translated to the rank case yet, or in
some cases have been proven after developing a different machinery. The aim of
this paper is to further relate both weights and generalized weights, show that
the results and proofs in both cases are usually essentially the same, and see
the significance of these similarities in network coding. Some of the new
results in the rank case also have new consequences in the Hamming case
MacWilliams Identities for Terminated Convolutional Codes
Shearer and McEliece [1977] showed that there is no MacWilliams identity for
the free distance spectra of orthogonal linear convolutional codes. We show
that on the other hand there does exist a MacWilliams identity between the
generating functions of the weight distributions per unit time of a linear
convolutional code C and its orthogonal code C^\perp, and that this
distribution is as useful as the free distance spectrum for estimating code
performance. These observations are similar to those made recently by
Bocharova, Hug, Johannesson and Kudryashov; however, we focus on terminating by
tail-biting rather than by truncation.Comment: 5 pages; accepted for 2010 IEEE International Symposium on
Information Theory, Austin, TX, June 13-1
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