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Successive cancellation list decoding parameter optimization method based on convolutional neural network

A technology of convolutional neural network and optimization method, which is applied in the direction of using convolutional code error correction/error detection, digital transmission system, data representation error detection/correction, etc., which can solve the problem of increasing complexity and the probability of failure of AD-SCL algorithm advanced questions

Inactive Publication Date: 2018-11-23
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

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  • Successive cancellation list decoding parameter optimization method based on convolutional neural network
  • Successive cancellation list decoding parameter optimization method based on convolutional neural network

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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 serial offset list decoding parameter optimization method based on a convolutional neural network, which mainly includes three parts: preparing sample data, building and training a convolutional neural network, and decoding. In the sample data preparation stage, the adaptive serial cancellation list decoding algorithm is firstly executed 100,000 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 records it, then randomly selects 60,000 sets of sample data, and finally randomly selects 75% of the 60,000 sets of data as training samples, and uses the remaining 25% of data as test samples; in the stage of building and training convolutional neural networks , first determine ...

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Abstract

The invention provides a successive cancellation list decoding parameter optimization method based on a convolutional neural network. The method comprises the steps of firstly starting from the operation of collecting and arranging sample data; then modeling based on characteristics and sizes of sample data, and training the network by using a counterpropagation algorithm; then computing a likelihood ratio by using a received signal, inputting the likelihood ratio into the trained convolutional neural network, and outputting Q; and at last, initializing L to be Q, and executing a successive cancellation list decoding algorithm. According to the method, the convolutional neural network technology and the polarization code decoding technology are combined, so that the unnecessary computationoperation is avoided and thus the decoding complexity of the polarization code is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of communication, and in particular relates to a method for optimizing polar code decoding parameters of a serial cancellation list decoding algorithm by using a convolutional 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 ...

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Application Information

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IPC IPC(8): H03M13/23H04L1/00
CPCH03M13/23H04L1/0057
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)