Turbo decoding method and device

Abstract

The invention provides a turbo decoding method and a turbo decoding method. In iteration of each time in the parallel decoding of a plurality of paths of code blocks to be decoded, the method comprises that: for a current path of code blocks to be decoded, a decoding module obtains all first intermediate variables of the current path in a backward recursion way, and simultaneously obtains all second intermediate variables of the current path in a forward recursion; the decoding module obtains all log likelihood ratios (LLR) of the current path according to all the obtained first intermediate variables, the second intermediate variables and all branch transfer measures of a module to be decoded; and the decoding module obtains prior information and decoding results output by the current path according to all the log likelihood ratios. By the turbo decoding method and the turbo decoding device, in each path of parallel decoding modules, backward and forward recursion operations are performed on the two intermediate variables at the same time respectively, thereby solving the problem of overlong time of iteration of each time in the conventional parallel decoding method, greatly shortening the decoding delay and improving the decoding speed.

Description

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AI Technical Summary

Benefits of technology

The technology described by this patented allows for faster processing times while reducing delays caused during data transmission between different parts (parallel decode) within an electronic device's memory module. This results in improved performance with reduced latency compared to previous methods like serial or pipelined techniques.

Problems solved by technology

This patented technical problem addressed in the patents relates to improving the efficiency and reliability of turbocodes in various applications such as 5thgeneration cellular networks. Current methods involve performing repeated calculations with significant delays when decoded from certain conditions. Additionally, current solutions have limited effectiveness because their algorithms require numerous repetitions before being able to achieve good results.

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