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Linear Network Error Correction Coding (Paperback, 2014 ed.)
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Linear Network Error Correction Coding (Paperback, 2014 ed.)
Series: SpringerBriefs in Computer Science
Expected to ship within 10 - 15 working days
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There are two main approaches in the theory of network error
correction coding. In this SpringerBrief, the authors summarize
some of the most important contributions following the classic
approach, which represents messages by sequences similar to
algebraic coding, and also briefly discuss the main results
following the other approach, that uses the theory of rank metric
codes for network error correction of representing messages by
subspaces. This book starts by establishing the basic linear
network error correction (LNEC) model and then characterizes two
equivalent descriptions. Distances and weights are defined in order
to characterize the discrepancy of these two vectors and to measure
the seriousness of errors. Similar to classical error-correcting
codes, the authors also apply the minimum distance decoding
principle to LNEC codes at each sink node, but use distinct
distances. For this decoding principle, it is shown that the
minimum distance of a LNEC code at each sink node can fully
characterize its error-detecting, error-correcting and
erasure-error-correcting capabilities with respect to the sink
node. In addition, some important and useful coding bounds in
classical coding theory are generalized to linear network error
correction coding, including the Hamming bound, the
Gilbert-Varshamov bound and the Singleton bound. Several
constructive algorithms of LNEC codes are presented, particularly
for LNEC MDS codes, along with an analysis of their performance.
Random linear network error correction coding is feasible for
noncoherent networks with errors. Its performance is investigated
by estimating upper bounds on some failure probabilities by
analyzing the information transmission and error correction.
Finally, the basic theory of subspace codes is introduced including
the encoding and decoding principle as well as the channel model,
the bounds on subspace codes, code construction and decoding
algorithms.
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