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Rapid optimization method for polarization code decoding parameters

An optimization method and polar code technology, applied in the field of polar code decoding parameters, can solve the problems of increased complexity and high probability of AD-SCL algorithm failure, and achieve the effect of reducing decoding complexity

Inactive Publication Date: 2018-11-09
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

Under the configuration of low signal-to-noise ratio and L=1, the probability of AD-SCL algorithm failure is high, so the L value needs to be updated frequently, which increases the complexity

Method used

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  • Rapid optimization method for polarization code decoding parameters
  • Rapid optimization method for polarization code decoding parameters

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Embodiment Construction

[0020] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0021] The invention provides a method for quickly optimizing decoding parameters of polar codes, which mainly includes three parts: preparing sample data, building and training a radial basis function neural network, and decoding. In the sample data preparation stage, the adaptive serial cancellation list decoding algorithm is firstly executed 50,000 times under different signal-to-noise ratios, and the likelihood ratio calculated from the received signal when decoding is successful and the corresponding likelihood ratio when decoding is successful L is recorded, and then 10,000 sets of sample data are randomly selected, and finally 75% of the 10,000 sets of data are randomly selected as training samples, and the remaining 25% of the data are used as test samples; In the network stage, first determine the hierarchical structure and parameters of the ne...

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Abstract

The invention provides a rapid optimization method for polarization code decoding parameters. The method comprises the following steps of firstly, starting from collection and sorting of sample data;then carrying out modeling according to the characteristics and the size of the sample data, and adopting a supervised learning and random gradient optimization method for training a network; then inputting the likelihood ratio obtained by calculation of the received signal into a radial basis function neural network model which completes the training and outputting M; and finally, initializing Lto M and executing a serial offset list decoding algorithm. According to the method, a radial basis function neural network technology and a polarization code decoding technology are combined, so thatunnecessary calculation operation is avoided, and therefore the coding complexity of polarization codes is greatly lowered.

Description

technical field [0001] The invention belongs to the technical field of communication, in particular to a method for optimizing polar code decoding parameters of a serial cancellation list decoding algorithm by using a radial basis function neural network. Background technique [0002] Polar code is a new type of channel coding proposed by E.Arikan in 2008. Polar codes are the first constructive coding schemes that can be proven mathematically to achieve channel capacity. When polar codes were proposed, Serial Cancellation (SC) decoding was also proposed. SC decoding can be viewed as a path search process on a binary tree. The SC decoding algorithm starts from the root node of the code tree and searches for the leaf node layer layer by layer. After each layer is expanded, the better one is selected from the two successors for expansion. There are two main characteristics of SC decoding. On the one hand, it has low complexity and simple decoding structure; on the other hand...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H03M13/13H04L1/00G06K9/62G06N3/08
CPCH03M13/13H04L1/0054H04L1/0057G06N3/084G06F18/2414
Inventor 李世宝卢丽金潘荔霞刘建航黄庭培陈海华邓云强
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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