150 research outputs found
Caching at the Edge with Fountain Codes
We address the use of linear randon fountain codes caching schemes in a
heterogeneous satellite network. We consider a system composed of multiple hubs
and a geostationary Earth orbit satellite. Coded content is memorized in hubs'
caches in order to serve immediately the user requests and reduce the usage of
the satellite backhaul link. We derive the analytical expression of the average
backhaul rate, as well as a tight upper bound to it with a simple expression.
Furthermore, we derive the optimal caching strategy which minimizes the average
backhaul rate and compare the performance of the linear random fountain code
scheme to that of a scheme using maximum distance separable codes. Our
simulation results indicate that the performance obtained using fountain codes
is similar to that of maximum distance separable codes
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
Sparse Network Coding with Overlapping Classes
This paper presents a novel approach to network coding for distribution of
large files. Instead of the usual approach of splitting packets into disjoint
classes (also known as generations) we propose the use of overlapping classes.
The overlapping allows the decoder to alternate between Gaussian elimination
and back substitution, simultaneously boosting the performance and reducing the
decoding complexity. Our approach can be seen as a combination of fountain
coding and network coding. Simulation results are presented that demonstrate
the promise of our approach.Comment: 15 pages, 5 figures, to be published at NetCod 200
Fountain Codes under Maximum Likelihood Decoding
This dissertation focuses on fountain codes under maximum likelihood (ML)
decoding. First LT codes are considered under a practical and widely used ML
decoding algorithm known as inactivation decoding. Different analysis
techniques are presented to characterize the decoding complexity. Next an upper
bound to the probability of decoding failure of Raptor codes under ML decoding
is provided. Then, the distance properties of an ensemble of fixed-rate Raptor
codes with linear random outer codes are analyzed. Finally, a novel class of
fountain codes is presented, which consists of a parallel concatenation of a
block code with a linear random fountain code.Comment: PhD Thesi
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