Polarization code belief propagation decoding method based on dynamic check matrix

Abstract

The invention discloses a polarization code belief propagation decoding method based on a dynamic check matrix to solve the problems of the prior art that performance is low and time complexity is high when a traditional BP decoding algorithm is used. The method comprises the steps of 1, setting module parameters; 2, initializing parameters; 3, adjusting the check matrix to be in a low-density form; 4, executing belief propagation decoding; 5, judging whether a codon estimation vector is an effective vector; 6, calculating the minimum value of an absolute log likelihood; 7, calculating a threshold value; 8, judging whether decoding succeeds; 9, outputting a decoding result. By adjusting the polarization code check matrix and reducing the number of short loops of the check matrix, rapid convergence of the BP algorithm is achieved, and low time complexity is maintained while polarization code decoding performance is improved by means of the early-stage stop standard.

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to a dynamic parity check matrix-based polarization code belief propagation decoding method in the technical field of channel coding. The present invention can be used to solve the problem of low performance and low performance in traditional belief propagation decoding of polar codes with medium and short code lengths in various communication systems such as mobile communication, personal communication, deep space communication, satellite communication and third-generation mobile communication. problems with high time complexity. Background technique [0002] Polar code is a new channel coding method proposed by E. Arikan based on the phenomenon of channel polarization. It has been proved in theory that it can reach the Shannon limit and has low computational complexity of coding and decoding. It is a major issue in the field of wireless communication. Breakthroughs ha...

AI Technical Summary

Problems solved by technology

Although this method improves the error correction capability of the communication system using polar codes as channel codecs, it still has shortcomings: after polarizing a limited number of channels, there are still some unpolarized channels

The disadvantages of this method are: under the improved algorithm, the frame error rate is still very high, and the decoding performance has not been significantly improved, and because of too many decoding iterations, the complexity of the algorithm is high

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