一种基于深度神经网络的译码器抽象方法
A Decoder Abstraction Method Based on Deep Neural Network
DOI: 10.12677/HJWC.2019.93013, PDF,   
作者: 许靖晖, 高月红, 杨鸿文：北京邮电大学，信息与通信工程学院，通信工程系，北京
关键词: 深度神经网络；译码器抽象；特征提取；Deep Neural Network； Decoder Abstraction； Feature Extraction
摘要: 在通信系统的仿真中，能否准确模拟出链路级译码结果对系统级仿真结果的可信度有重要意义。本文提出一种基于深度神经网络的译码器抽象方法，该方法从译码器的软输入中提取三个特征量，借助神经网络模型来预测码字的译码是否成功。仿真结果表明，本文所提出的基于深度神经网络的方法比EESM等传统方法有更好的预测精度。
Abstract: In the simulation of communication system, it is important to simulate the link decoding result accurately. This paper proposes a decoder abstraction method based on deep neural network (DNN), which extracts three features from the soft input of the decoder and uses the neural network model to predict the decoding success. Simulation results show that the method proposed in this paper has better prediction accuracy than traditional methods such as EESM.
文章引用：许靖晖, 高月红, 杨鸿文. 一种基于深度神经网络的译码器抽象方法[J]. 无线通信, 2019, 9(3): 105-111. https://doi.org/10.12677/HJWC.2019.93013

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