A Penalized Dual Decomposition Channel Decoding Method Aided by Multilayer Neural Network

Abstract

The invention provides a penalty dual decomposition channel decoding method assisted by a multi-layer neural network, which is mainly oriented to binary linear codes. The method includes the following steps: (1) Constructing the maximum likelihood optimization problem based on channel decoding; (2) Transforming the maximum likelihood optimization problem of channel decoding into parity-check polyhedron-based decoding by introducing the concept of basic polyhedron into constraints code optimization problem; (3) introduce the solution penalty dual decomposition method to solve the decoding optimization problem, and obtain the penalty dual decomposition channel decoder; (4) design a check polyhedron map based on a multi-layer neural network, and obtain learning parameters through training, The multi-layer neural network-based check polyhedron mapping is introduced into the penalty dual decomposition channel decoder, and the multi-layer neural network-assisted penalty dual decomposition channel decoder is obtained. The present invention further improves the decoding performance and reduces the decoding delay by means of the power of machine learning.

Description

technical field [0001] The invention belongs to the field of wireless communication channel coding and decoding, and relates to a penalty dual decomposition channel decoding method assisted by a multi-layer neural network. Background technique [0002] Channel decoding is how to judge the received symbol message. In a general information transmission system, the message received by the sink is not necessarily the same as the message sent by the source, and the sink needs to know which source message is sent by the source at this time, so the message received by the sink needs to be based on a certain A rule judges that it corresponds to one of the source symbol message sets. Linear programming (LP) decoders are based on a linear relaxation of the original maximum likelihood decoding problem and are a popular decoding technique for binary linear codes. Since linear program decoders have strong guarantees on decoding performance in theory, they have attracted extensive atten...

AI Technical Summary

Problems solved by technology

However, compared with the classical Belief Propagation (BP) decoder, the LP decoder has higher computational complexity and lower error correction performance in the low signal-to-noise ratio (SNR) region

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