CommUnet：通信中卷积码的U网解码器
CommUnet: U-net decoder for convolutional codes in communication
摘要：
近年来，深度神经网络在解决二维图像处理中的各种挑战方面发挥了重要作用。全卷积网络（FCN）（如U-net）已被证明在医学图像分析和在黑暗场所拍摄的图像去噪的分割任务中非常成功。本文利用这种著名的深度神经网络来应对最近被证明适用于深度神经网络的信道解码挑战。以前的工作已经成功地使用不同的架构来解码卷积码，例如递归神经网络（RNN）和全连接神经网络（FCNN），并取得了令人满意的结果。然而，这些方法在延迟方面代价高昂，计算资源和存储器。本文表明，采用二维图像处理中使用的方法，通过在预处理阶段对数据进行简单的操作，在误比特率（BER）测量中实现了更好的结果，并且在延迟和维护神经解码器所需的参数数量上有很大的折扣。
英文摘要：
In recent years, deep neural networks have played a major role solving various challenges in two dimensional image processing.Fully Convolutional Networks (FCN) such as U-net have been shown to be highly successful at segmentation tasks for medical images analysis and denoising images taken in dark venues. This paper harnesses this well-known deep neural network for the channel decoding challenge recently proven to be suitable for deep neural networks.Previous work have successfully managed to decode convolutional codes using different architectures,such as Recurrent Neural Networks(RNN) and Fully Connected Neural Networks(FCNN) with promising results.However,these approaches are extremely costly in latency,computational resources and memory.This paper shows that taking the approach used in two dimensional image processing,by simple manipulation on the data in the preprocessing phase,achieves better results in a Bit Error Rate(BER) measurement with a large discount on the latency and the number of parameters required to maintain the neural decoder.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
