Polarization code flipping decoding method and system based on deep learning

Abstract

The invention discloses a polarization code flipping decoding method and system based on deep learning, and belongs to the field of wireless communication. The method comprises the following steps: firstly, constructing a neural network unit and training, and then inputting soft information output by a decoding unit into the neural network unit to obtain a possible decoding error position; and then feeding back a possible decoding error position to the decoding unit, overturning a decoding result of the decoding error position by the decoding unit, and then restarting decoding until a decodingresult is obtained. The system comprises a neural network unit and a decoding unit, wherein the neural network unit is connected with the decoding unit; the decoding unit comprises a belief propagation unit, a verification unit and a turnover unit. According to the invention, the defects of high complexity and unsatisfactory performance of the traditional flip decoding algorithm in the prior artare solved, the decoding complexity is reduced, and different channel environments and configuration requirements of a communication system are met.

Description

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AI Technical Summary

Benefits of technology

This patented technology describes an improved way to decode binary codes (BCH) accurately without having any issues or drawbacks associated therewith. It uses advanced techniques like convolutional coding and artificial intelligence algorithms to create models from training data sets called neurons. These modeling tools help researchers better identify patterns within these datasets and make more accurate predictions about how likely they will behave when transmitting digital messages over wireless networks. Overall, this new approach helps optimize signal transmission efficiency while maintaining accuracy and reliance levels.

Problems solved by technology

This patented technology describes different techniques called symbolic loop convolution decode(SLCC). These technologies aim at solving issues such as limited processing speed caused by slow convergence time and difficulty in achieving accurate decisions even if implemented efficiently over short distances. Specifically, SLCODEC proposes a new approach where instead of performing iteratively hard decision iteration, each step takes place before applying any further calculations until reaching certain thresholds. By doing this we mean reducing computational load while maintaining accuracy.

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