Decoding method and decoding device

Abstract

The invention discloses a decoding method and a decoding device. The decoding method is based on at least one sub-decoder, and each sub-decoder can perform serial decoding treatment on N decoding sub-data blocks. The decoding device comprises at least one sub-decoder, and each sub-decoder can perform serial decoding treatment on N decoding sub-data blocks; the sub-decoder comprises an input module, a decoding module and an output module. In the decoding device, each sub-decoder can perform serial decoding treatment on N decoding sub-data blocks, and in the decoding process, the data in different decoding sub-data blocks can be decoded at the same time during each clock period, and compared with the prior art in which only one data can be decoded during each clock period and one data can be completely decoded during a plurality of clock periods, the embodiment of the invention improves the decoding throughput rate of Turbo codes on the premise of not increasing consumption of logic resources.

Description

technical field [0001] The embodiment of the present invention relates to the communication field, and in particular to a decoding method and a decoding device. Background technique [0002] Shannon's 1948 paper titled "A Mathematical Theory of Communication" laid the foundation for modern communication theory. The coding theorem he proposed pointed out that if a sufficiently long random code is used, the information can be transmitted over the channel capacity C Error-free transmission with minimal redundancy. Shannon's coding theorem raises two practical problems: first, random coding that can reach the channel capacity C exists in theory, but the coding theorem does not clearly point out how to construct the random coding; second, for random coding, increasing Its length can improve the transmission performance, but the complexity of decoding the random code at the decoding end is proportional to the length of the random code, that is, the longer the length of the random...

AI Technical Summary

Problems solved by technology

Shannon's coding theorem raises two practical problems: first, random coding that can reach the channel capacity C exists in theory, but the coding theorem does not clearly point out how to construct the random coding; second, for random coding, increasing Its length can improve the transmission performance, but the complexity of decoding the random code at the decoding end is proportional to the length of the random code, that is, the longer the length of the random code, the higher the complexity of decoding it. Therefore, looking for A suitable decoding method is also difficult

However, there is a feedback loop in the logic circuit of the Turbo code decoder. When decoding, it is necessary to use the data generated in the previous clock cycle to decode the data in the next clock cycle. In other words, only one data can be decoded in each clock cycle. Decoding processing and multiple clock cycles can only complete the decoding process of one data, where the number of clock cycles depends on the number of pipeline stages, so it is difficult to improve the decoding of Turbo codes only by using the traditional method of adding pipelines in logic circuits Throughput

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