论文标题
通过深度学习解码5G-NR通信
Decoding 5G-NR Communications via Deep Learning
论文作者
论文摘要
即将到来的现代通信基于5G规格，旨在为新型垂直行业提供解决方案。物理层的主要变化之一是使用低密度平价检查（LDPC）代码进行通道编码。尽管LDPC代码与上一代相比引入了其他计算复杂性，而使用的Turbocodes在使用的情况下，LDPC代码就复杂性 - 位错误率（BER）方面提供了合理的权衡。与此同时，深度学习算法正在经历新的革命，特别是图像和视频处理。在这种情况下，可以在无线电通信中利用一些方法。在本文中，我们建议将自动编码神经网络（ANN）与深神经网络（DNN）联合使用，以构建自动编码深神经网络（ADNN）进行拆除和解码。结果将公布，对于特定的BER目标，需要在附加的白色高斯噪声（AWGN）频道中少$ 3 $ dB的信号与噪声比（SNR）。
Upcoming modern communications are based on 5G specifications and aim at providing solutions for novel vertical industries. One of the major changes of the physical layer is the use of Low-Density Parity-Check (LDPC) code for channel coding. Although LDPC codes introduce additional computational complexity compared with the previous generation, where Turbocodes where used, LDPC codes provide a reasonable trade-off in terms of complexity-Bit Error Rate (BER). In parallel to this, Deep Learning algorithms are experiencing a new revolution, specially to image and video processing. In this context, there are some approaches that can be exploited in radio communications. In this paper we propose to use Autoencoding Neural Networks (ANN) jointly with a Deep Neural Network (DNN) to construct Autoencoding Deep Neural Networks (ADNN) for demapping and decoding. The results will unveil that, for a particular BER target, $3$ dB less of Signal to Noise Ratio (SNR) is required, in Additive White Gaussian Noise (AWGN) channels.
