Nonuniform quantizing coding method used for decoding LDPC code and application of method in decoder

Abstract

The invention relates to a nonuniform quantizing coding method used for decoding an LDPC code and the application of the method in a decoder. The method comprises the steps that the quantification range of messages L(rji), L(qij) and L(Qi) is determined according to information distribution of an initial message L(Pi), a coding mode is adopted for performing nonuniform quantizing on the messages L(rji), L(qij) and L(Qi), quantized intervals of positive number portions and negative number portions are respectively divided into m sections, coding is a codon composed of 1-bit polar codes, k-bit segment codes and (n-1-k)-bit in-segment codes, the polar codes mark positive numbers and negative numbers, the segment codes mark different segments, the segment internal codes show specific size of the data in the segments, each segment is divided into 2<g> small segments, and the small segments are the quantized intervals of the corresponding segments. According to the method, consumption of hardware storage resources, operation processing units and locating and wiring resources is reduced, quantization errors of small-value data are reduced, and the decoding performance is improved.

Description

technical field [0001] The invention relates to the technical field of LDPC code decoding, in particular to a non-uniform quantization encoding method for LDPC code decoding and its application in a decoder. Background technique [0002] The most classic LDPC code decoding algorithm is the Belief-propagation (BP) algorithm. In engineering, a simplified algorithm based on the BP algorithm—minimum-sum algorithm and its various correction algorithms are generally used. In practical applications, LDPC code decoders deal with digital signals, and data quantization and truncation are inevitable processing methods, and the errors caused by quantization and truncation will directly affect the decoding performance of LDPC codes. In addition, the LDPC code belief propagation algorithm needs to store a large amount of intermediate data. Since the code length of the LDPC code is very long, even if various optimization algorithms are used, the amount of data processed by the decoder is s...

AI Technical Summary

Problems solved by technology

This quantization method reduces the number of quantization bits to a certain extent, but its essence is still uniform quantization, and the quantization error of small-value data is relatively large in the next few iterations.

Moreover, the expansion speed of data in the decoding process is not constant, and it is closely related to the signal-to-noise ratio and the number of iterations of correct decoding. It is difficult to accurately determine how many iterations to change the quantization range, and it is even more difficult to determine the value of the expansion factor.

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