基于卷积神经网络的极坐标解码

Yue Qin, Feng Liu
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引用次数: 3

摘要

在这项工作中,我们将卷积神经网络(CNN)的能力扩展到极码解码。先前的研究表明,多层感知器(MLP)作为深度神经网络(DNN)的一种基本形式,可以在极码块长度很短的情况下实现较高的解码精度和解码速度。然而,由于庞大的网络结构,它的性能在较长的代码中急剧下降。在这项工作中,我们设计并实现了一个用于极性解码的CNN。为了提高解码精度,我们根据极性码的编码结构在CNN输出中引入辅助标签。此外,我们提出对CNN进行修剪,以保持更广泛网络的解码精度,同时减少计算量和参数。通过这两项创新,可以提高原始CNN的解码精度。我们进行了大量的仿真来比较我们设计的CNN解码器和MLP解码器。结果表明,当编码长度为64时,我们的模型比MLP解码器小60倍,并且随着模型尺寸的增加,我们的模型精度增加,而MLP达到饱和。另外的结果表明,在边际参数增加的情况下,我们提出的方法在误码率方面优于原始CNN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolutional Neural Network-Based Polar Decoding
In this work, we extend the capability of convolutional neural network (CNN) to polar code decoding. Previous work has shown that a multi-layer perceptron (MLP), which is a basic form of deep neural network (DNN), can achieve high decoding accuracy and speed for polar code when the block length is very short. However, its performance drops drastically for longer codes, due to the bulky network structure. In this work, we design and implement a CNN for polar decoding. In order to improve the decoding accuracy, we introduce auxiliary labels into CNN output based on the encoding structure of polar code. In addition, we propose to prune the CNN to preserve the decoding accuracy of wider network while reducing the computation and the parameters. With these two innovations, the decoding accuracy of original CNN can be improved. We carry out extensive simulations to compare our designed CNN decoder with MLP decoder. Results show that when the code length is 64, our model is 60 times smaller than the MLP decoder, and the accuracy of our model increases with model size, while MLP reaches saturation. Additional results show that our proposed method outperforms original CNN with regard to the BER performance under marginal parameter increase.
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