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Improved polar code SCF decoder based on genetic algorithm

A genetic algorithm and polar code technology, applied in genetic rules, cyclic codes, genetic models, etc., can solve problems such as high decoding delay and computational complexity

Pending Publication Date: 2020-11-24
CHINA JILIANG UNIV
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Problems solved by technology

However, the SCL decoder retains multiple decoding paths when decoding. This decoding feature makes the SCL decoder have a high decoding delay and computational complexity.

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  • Improved polar code SCF decoder based on genetic algorithm
  • Improved polar code SCF decoder based on genetic algorithm
  • Improved polar code SCF decoder based on genetic algorithm

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Embodiment Construction

[0016] The present invention will be described in further detail below in conjunction with the accompanying drawings. The following examples are helpful to the understanding of the present invention and are better application examples, but should not be regarded as a limitation of the present invention.

[0017] exist figure 1 Among them, the system block diagram is mainly composed of polar code encoding module, channel module and polar code SCF decoding module. In addition, the polar code SCF decoding module is composed of a standard SC decoding module, a CRC check module and a candidate flip bit construction module based on a genetic algorithm. The channel module uses additive white Gaussian noise. The length of the CRC check bit in the CRC check module is 16, and the CRC generating polynomial is g(x)=x 16 +x 15 +x 2 +1. The genetic algorithm technology belonging to the field of artificial intelligence is used in the candidate flip position building block. Such as fi...

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Abstract

The invention provides an improved polar code serial cancellation flip (SCF) decoder based on a genetic algorithm (GA), and particularly relates to a polar code successive cancellation flip (SCF) decoder based on the genetic algorithm (GA). On the basis of an original SCF decoder, aiming at the problem that redundancy exists in an original CFPS (Candidate Flipping Position Set), a new CFPS is constructed by utilizing GA. An initial population of the genetic algorithm is formed by using indexes of all non-frozen bits, and the channel reliability calculated by Gaussian approximation is taken asthe fitness of each individual. Continuous selection, crossover and mutation operations are carried out on the population, and the optimal individual of each generation of population can be stored. Finally, a new candidate flipping position set CFPS-GA is obtained by counting the occurrence frequency of each population in the vector, and SCF decoding is carried out by using the newly constructed candidate flipping position set CFPS-GA. The SCF decoder has the beneficial effects that compared with other similar SCF decoders, the CFPS-GA-based SCF decoder has lower computation complexity and decoding delay on the premise of ensuring the decoding performance.

Description

technical field [0001] The invention belongs to the field of channel coding and decoding, and relates to a polar code serial cancellation flip (Successive Cancellation Flip, SCF) decoder and a genetic algorithm in artificial intelligence technology. Background technique [0002] Polar codes have been developed since 2009 by After being proposed, it has received widespread attention, and in the newly released 5G communication standard, polar code is selected as the coding scheme under the control channel of the eMBB scene. Polar codes are currently the only channel coding schemes that have been theoretically proven to reach the limit of Shannon's theory. The most primitive polar code decoder is a serial cancellation (SuccessiveCancellation, SC) decoder, and it is under this decoder that the polar code can reach the Shannon theoretical limit. However, the SC decoder is a serial decoder. If an error occurs in the decoding of the previous bits, it will affect the decoding of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M13/15G06N3/12
CPCH03M13/15G06N3/126
Inventor 王秀敏马强强李君张鸿超
Owner CHINA JILIANG UNIV
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