Fountain code decoding method suitable for radio broadcasting system

Abstract

The invention provides a fountain code decoding method suitable for a radio broadcasting system. The decoding method includes: step one, calcifying signals after passing a channel decoder into a group with residual errors and a group without residual errors at the receiving end; step two, for the group without the residual errors, setting a log likelihood ratio (LLR) value with a corresponding bit of zero as a large positive number (such as the positive infinity) and an LLR value with a corresponding bit of one as a small negative number (such as the negative infinity), and collecting LLR values of the group with the residual errors to participate in the decoding process of the function code; and step three, trying to decode all source symbol sets through hard iteration of a coded symbol with certain messages and updating a bipartite graph of the fountain code in a bit domain and an LLR domain during the iteration. The method has the advantages of high spectrum efficiency due to retention of the group with the residual errors and low decoding complexity.

Description

technical field [0001] The present invention relates to fountain codes, in particular to a fountain code decoding method suitable for wireless broadcasting systems. Background technique [0002] Fountain codes are a class of rateless codes that can generate an infinite number of encoding symbols (Ecoding Symbols) from a given set of source symbols (Source Symbols), and the source symbols can be recovered from any subset of encoding symbols , as long as this subset has the same size as the source symbol set. Suppose, there is U=[u at the coding end 1 ,u 2 ,...,u K ] need to be transmitted, and generate C=[c by fountain code encoding 1 ,c 2 ,...,c N ] and sent to the channel, so that as long as the receiving end obtains C from the channel = [c 1 ,c 2 ,...,c N ], any subset of size K can be successfully decoded. [0003] In 2005, Luby et al. proposed LT (Luby Transform) codes, whose performance is close to fountain codes. With this encoding method, the receiving end ...

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Problems solved by technology

[0010] The decoding algorithm provided by the prior art 1 has obvious disadvantages: the packet X' that still has residual errors after passing through the convolutional code channel decoding module at the receiving end will be discarded

[0013] The LT sum product soft decoding algorithm in the prior art 2 is essentially a maximum likelihood probability iterative algorithm, although it improves the transmission efficiency compared with the technology 1, it has a very high complexity

[0014] In summary, there are mainly two types of existing LT code decoding algorithms. The information iteration algorithm based on the erasure channel model in prior art 1 has relatively low complexity, but the transmission efficiency is low; prior art 2 The sum-product soft decoding algorithm has high transmission efficiency, but high complexity

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