Jielin code error correction optimization method and device

Abstract

The invention discloses a Jielin code error correction optimization method and device. The method comprises the steps: S100, obtaining to-be-coded binary data, carrying out the bit-by-bit calculationof the to-be-coded binary data through a first constraint condition based on a weighted probability model to obtain the coded data; then obtaining the bit length of the binary data to be coded and thefirst total number of symbols 0, and sending the coded data, the bit length and the first total number to a channel for data transmission; and S200, receiving the coded data, the bit length and the first total number from the channel, decoding the coded data based on the weighted probability model, checking a decoding result through the first constraint condition, and performing forward error correction processing on the decoding result. According to the invention, the data transmitted in the channel is linearly encoded and decoded through the weighted probability model, so that the requirement on hardware resources is low, the error correction speed is improved, and the error correction efficiency is further improved.

Description

technical field [0001] The invention relates to the technical field of error correction for data transmission, in particular to a method and device for error correction optimization of a Jelling code. Background technique [0002] In order to construct a coding method that approximates the channel capacity, experts and scholars have made unremitting efforts. In 2009, Arikan proposed a coding method based on the channel polarization phenomenon, which is strictly proved to be capacity-reachable when the code length approaches infinity, called polar code (Polar Code). LDPC code, Turbo code can be close to the Shannon limit. Polar codes in 5G communication systems usually use cyclic redundancy codes (CRC) to implement error checking, but when Polar codes and LDPC codes need to achieve the optimal error correction rate and computational complexity under a certain code length, the hardware resource requirements Both are relatively large, and the error correction efficiency is lo...

AI Technical Summary

Problems solved by technology

Polar codes in 5G communication systems usually use cyclic redundancy codes (CRC) to implement error checking, but when Polar codes and LDPC codes need to achieve the optimal error correction rate and computational complexity under a certain code length, the hardware resource requirements Both are relatively large, and the error correction efficiency is low

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