27,684 research outputs found
On Pseudocodewords and Improved Union Bound of Linear Programming Decoding of HDPC Codes
In this paper, we present an improved union bound on the Linear Programming
(LP) decoding performance of the binary linear codes transmitted over an
additive white Gaussian noise channels. The bounding technique is based on the
second-order of Bonferroni-type inequality in probability theory, and it is
minimized by Prim's minimum spanning tree algorithm. The bound calculation
needs the fundamental cone generators of a given parity-check matrix rather
than only their weight spectrum, but involves relatively low computational
complexity. It is targeted to high-density parity-check codes, where the number
of their generators is extremely large and these generators are spread densely
in the Euclidean space. We explore the generator density and make a comparison
between different parity-check matrix representations. That density effects on
the improvement of the proposed bound over the conventional LP union bound. The
paper also presents a complete pseudo-weight distribution of the fundamental
cone generators for the BCH[31,21,5] code
Improving success probability and embedding efficiency in code based steganography
For stegoschemes arising from error correcting codes, embedding depends on a
decoding map for the corresponding code. As decoding maps are usually not
complete, embedding can fail. We propose a method to ensure or increase the
probability of embedding success for these stegoschemes. This method is based
on puncturing codes. We show how the use of punctured codes may also increase
the embedding efficiency of the obtained stegoschemes
Maximum-likelihood decoding of Reed-Solomon Codes is NP-hard
Maximum-likelihood decoding is one of the central algorithmic problems in
coding theory. It has been known for over 25 years that maximum-likelihood
decoding of general linear codes is NP-hard. Nevertheless, it was so far
unknown whether maximum- likelihood decoding remains hard for any specific
family of codes with nontrivial algebraic structure. In this paper, we prove
that maximum-likelihood decoding is NP-hard for the family of Reed-Solomon
codes. We moreover show that maximum-likelihood decoding of Reed-Solomon codes
remains hard even with unlimited preprocessing, thereby strengthening a result
of Bruck and Naor.Comment: 16 pages, no figure
The problem with the SURF scheme
There is a serious problem with one of the assumptions made in the security
proof of the SURF scheme. This problem turns out to be easy in the regime of
parameters needed for the SURF scheme to work.
We give afterwards the old version of the paper for the reader's convenience.Comment: Warning : we found a serious problem in the security proof of the
SURF scheme. We explain this problem here and give the old version of the
paper afterward
Structured Random Linear Codes (SRLC): Bridging the Gap between Block and Convolutional Codes
Several types of AL-FEC (Application-Level FEC) codes for the Packet Erasure
Channel exist. Random Linear Codes (RLC), where redundancy packets consist of
random linear combinations of source packets over a certain finite field, are a
simple yet efficient coding technique, for instance massively used for Network
Coding applications. However the price to pay is a high encoding and decoding
complexity, especially when working on , which seriously limits the
number of packets in the encoding window. On the opposite, structured block
codes have been designed for situations where the set of source packets is
known in advance, for instance with file transfer applications. Here the
encoding and decoding complexity is controlled, even for huge block sizes,
thanks to the sparse nature of the code and advanced decoding techniques that
exploit this sparseness (e.g., Structured Gaussian Elimination). But their
design also prevents their use in convolutional use-cases featuring an encoding
window that slides over a continuous set of incoming packets.
In this work we try to bridge the gap between these two code classes,
bringing some structure to RLC codes in order to enlarge the use-cases where
they can be efficiently used: in convolutional mode (as any RLC code), but also
in block mode with either tiny, medium or large block sizes. We also
demonstrate how to design compact signaling for these codes (for
encoder/decoder synchronization), which is an essential practical aspect.Comment: 7 pages, 12 figure
Computing coset leaders and leader codewords of binary codes
In this paper we use the Gr\"obner representation of a binary linear code
to give efficient algorithms for computing the whole set of coset
leaders, denoted by and the set of leader codewords,
denoted by . The first algorithm could be adapted to
provide not only the Newton and the covering radius of but also to
determine the coset leader weight distribution. Moreover, providing the set of
leader codewords we have a test-set for decoding by a gradient-like decoding
algorithm. Another contribution of this article is the relation stablished
between zero neighbours and leader codewords
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