414 research outputs found
Inactivation Decoding of LT and Raptor Codes: Analysis and Code Design
In this paper we analyze LT and Raptor codes under inactivation decoding. A
first order analysis is introduced, which provides the expected number of
inactivations for an LT code, as a function of the output distribution, the
number of input symbols and the decoding overhead. The analysis is then
extended to the calculation of the distribution of the number of inactivations.
In both cases, random inactivation is assumed. The developed analytical tools
are then exploited to design LT and Raptor codes, enabling a tight control on
the decoding complexity vs. failure probability trade-off. The accuracy of the
approach is confirmed by numerical simulations.Comment: Accepted for publication in IEEE Transactions on Communication
Bounds on the Error Probability of Raptor Codes under Maximum Likelihood Decoding
In this paper upper and lower bounds on the probability of decoding failure
under maximum likelihood decoding are derived for different (nonbinary) Raptor
code constructions. In particular four different constructions are considered;
(i) the standard Raptor code construction, (ii) a multi-edge type construction,
(iii) a construction where the Raptor code is nonbinary but the generator
matrix of the LT code has only binary entries, (iv) a combination of (ii) and
(iii). The latter construction resembles the one employed by RaptorQ codes,
which at the time of writing this article represents the state of the art in
fountain codes. The bounds are shown to be tight, and provide an important aid
for the design of Raptor codes.Comment: Submitted for revie
Decentralized Erasure Codes for Distributed Networked Storage
We consider the problem of constructing an erasure code for storage over a
network when the data sources are distributed. Specifically, we assume that
there are n storage nodes with limited memory and k<n sources generating the
data. We want a data collector, who can appear anywhere in the network, to
query any k storage nodes and be able to retrieve the data. We introduce
Decentralized Erasure Codes, which are linear codes with a specific randomized
structure inspired by network coding on random bipartite graphs. We show that
decentralized erasure codes are optimally sparse, and lead to reduced
communication, storage and computation cost over random linear coding.Comment: to appear in IEEE Transactions on Information Theory, Special Issue:
Networking and Information Theor
Binary Systematic Network Coding for Progressive Packet Decoding
We consider binary systematic network codes and investigate their capability
of decoding a source message either in full or in part. We carry out a
probability analysis, derive closed-form expressions for the decoding
probability and show that systematic network coding outperforms conventional
network coding. We also develop an algorithm based on Gaussian elimination that
allows progressive decoding of source packets. Simulation results show that the
proposed decoding algorithm can achieve the theoretical optimal performance.
Furthermore, we demonstrate that systematic network codes equipped with the
proposed algorithm are good candidates for progressive packet recovery owing to
their overall decoding delay characteristics.Comment: Proc. of IEEE ICC 2015 - Communication Theory Symposium, to appea
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