论文标题
社区：通信中卷积代码的U-NET解码器
CommUnet: U-net decoder for convolutional codes in communication
论文作者
论文摘要
近年来，深度神经网络在二维图像处理中发挥了重要作用，解决了各种挑战。卷积网络（FCN）（例如U-NET）已被证明在用于医疗图像分析的分割任务方面非常成功，并在黑暗场所拍摄的图像。本文利用这种众所周知的深神经网络，用于最近被证明适用于深层神经网络的渠道解码挑战。预防的工作已成功地设法使用不同的架构来解码卷积代码，例如复发性神经网络（RNN）（RNN）（RNN）（RNN）和完全连接的神经网络（FCNN）具有有益的结果。在二维图像处理中，通过对预处理阶段中的数据进行简单操纵，可以获得更好的结果误差率（BER）测量，并享受延迟的折扣和维护神经解码器所需的参数数量。
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.
