Publication

Systematic encoding and decoding of chain reaction codes

Mohammad Amin Shokrollahi
2005
Patents

Résumé

A method of encoding data into a chain reaction code includes generating a set of input symbols from input data. Subsequently, one or more non-systematic output symbols is generated from the set of input symbols, each of the one or more non-systematic output symbols being selected from an alphabet of non-systematic output symbols, and each non-systematic output symbol generated as a function of one or more of the input symbols. As a result of this encoding process, any subset of the set of input symbols is recoverable from (i) a predetermined number of non-systematic output symbols, or (ii) a combination of (a) input symbols which are not included in the subset of input symbols that are to be recovered, and (b) one or more of the non-systematic output symbols.

Source officielle

https://infoscience.epfl.ch/entities/publication/38599b8a-ffb9-4c99-a9d3-f9e61ee91e49

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Mohammad Amin Shokrollahi
2005
Patents

Résumé

Source officielle

https://infoscience.epfl.ch/entities/publication/38599b8a-ffb9-4c99-a9d3-f9e61ee91e49

À propos de ce résultat

Concepts associés (13)

Convolutional code

In telecommunication, a convolutional code is a type of error-correcting code that generates parity symbols via the sliding application of a boolean polynomial function to a data stream. The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'. The sliding nature of the convolutional codes facilitates trellis decoding using a time-invariant trellis. Time invariant trellis decoding allows convolutional codes to be maximum-likelihood soft-decision decoded with reasonable complexity.

Systematic code

In coding theory, a systematic code is any error-correcting code in which the input data is embedded in the encoded output. Conversely, in a non-systematic code the output does not contain the input symbols. Systematic codes have the advantage that the parity data can simply be appended to the source block, and receivers do not need to recover the original source symbols if received correctly – this is useful for example if error-correction coding is combined with a hash function for quickly determining the correctness of the received source symbols, or in cases where errors occur in erasures and a received symbol is thus always correct.

Fountain code

In coding theory, fountain codes (also known as rateless erasure codes) are a class of erasure codes with the property that a potentially limitless sequence of encoding symbols can be generated from a given set of source symbols such that the original source symbols can ideally be recovered from any subset of the encoding symbols of size equal to or only slightly larger than the number of source symbols. The term fountain or rateless refers to the fact that these codes do not exhibit a fixed code rate.

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From LDPC Block to LDPC Convolutional Codes: Capacity, Stability, and Universality

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