12 research outputs found
Skew and linearized Reed-Solomon codes and maximum sum rank distance codes over any division ring
Reed-Solomon codes and Gabidulin codes have maximum Hamming distance and
maximum rank distance, respectively. A general construction using skew
polynomials, called skew Reed-Solomon codes, has already been introduced in the
literature. In this work, we introduce a linearized version of such codes,
called linearized Reed-Solomon codes. We prove that they have maximum sum-rank
distance. Such distance is of interest in multishot network coding or in
singleshot multi-network coding. To prove our result, we introduce new metrics
defined by skew polynomials, which we call skew metrics, we prove that skew
Reed-Solomon codes have maximum skew distance, and then we translate this
scenario to linearized Reed-Solomon codes and the sum-rank metric. The theories
of Reed-Solomon codes and Gabidulin codes are particular cases of our theory,
and the sum-rank metric extends both the Hamming and rank metrics. We develop
our theory over any division ring (commutative or non-commutative field). We
also consider non-zero derivations, which give new maximum rank distance codes
over infinite fields not considered before
Error-Erasure Decoding of Linearized Reed-Solomon Codes in the Sum-Rank Metric
Codes in the sum-rank metric have various applications in error control for
multishot network coding, distributed storage and code-based cryptography.
Linearized Reed-Solomon (LRS) codes contain Reed-Solomon and Gabidulin codes as
subclasses and fulfill the Singleton-like bound in the sum-rank metric with
equality. We propose the first known error-erasure decoder for LRS codes to
unleash their full potential for multishot network coding. The presented
syndrome-based Berlekamp-Massey-like error-erasure decoder can correct
full errors, row erasures and column erasures up to in the sum-rank metric requiring at most
operations in , where is the code's length and its
dimension. We show how the proposed decoder can be used to correct errors in
the sum-subspace metric that occur in (noncoherent) multishot network coding.Comment: 6 pages, presented at ISIT 202
Bounds and Genericity of Sum-Rank-Metric Codes
We derive simplified sphere-packing and Gilbert-Varshamov bounds for codes in
the sum-rank metric, which can be computed more efficently than previous
ones.They give rise to asymptotic bounds that cover the asymptotic setting that
has not yet been considered in the literature: families of sum-rank-metric
codes whose block size grows in the code length. We also provide two genericity
results: we show that random linear codes achieve almost the sum-rank-metric
Gilbert-Varshamov bound with high probability. Furthermore, we derive bounds on
the probability that a random linear code attains the sum-rank-metric Singleton
bound, showing that for large enough extension field, almost all linear codes
achieve it
Convolutional Codes in Rank Metric with Application to Random Network Coding
Random network coding recently attracts attention as a technique to
disseminate information in a network. This paper considers a non-coherent
multi-shot network, where the unknown and time-variant network is used several
times. In order to create dependencies between the different shots, particular
convolutional codes in rank metric are used. These codes are so-called
(partial) unit memory ((P)UM) codes, i.e., convolutional codes with memory one.
First, distance measures for convolutional codes in rank metric are shown and
two constructions of (P)UM codes in rank metric based on the generator matrices
of maximum rank distance codes are presented. Second, an efficient
error-erasure decoding algorithm for these codes is presented. Its guaranteed
decoding radius is derived and its complexity is bounded. Finally, it is shown
how to apply these codes for error correction in random linear and affine
network coding.Comment: presented in part at Netcod 2012, submitted to IEEE Transactions on
Information Theor
Fast Decoding of Codes in the Rank, Subspace, and Sum-Rank Metric
We speed up existing decoding algorithms for three code classes in different
metrics: interleaved Gabidulin codes in the rank metric, lifted interleaved
Gabidulin codes in the subspace metric, and linearized Reed-Solomon codes in
the sum-rank metric. The speed-ups are achieved by reducing the core of the
underlying computational problems of the decoders to one common tool: computing
left and right approximant bases of matrices over skew polynomial rings. To
accomplish this, we describe a skew-analogue of the existing PM-Basis algorithm
for matrices over usual polynomials. This captures the bulk of the work in
multiplication of skew polynomials, and the complexity benefit comes from
existing algorithms performing this faster than in classical quadratic
complexity. The new faster algorithms for the various decoding-related
computational problems are interesting in their own and have further
applications, in particular parts of decoders of several other codes and
foundational problems related to the remainder-evaluation of skew polynomials