论文标题
混合神经编码调制：设计和训练方法
Hybrid Neural Coded Modulation: Design and Training Methods
论文作者
论文摘要
我们提出了一个组成内部和外部代码的混合编码调制方案。外壳可以是具有有效的软解码功能（例如低密度均衡检查（LDPC）代码）的任何标准二进制线性代码。内部代码是使用深神经网络（DNN）设计的，该网络（DNN）采用通道编码的位和输出调制符号。对于训练DNN，我们建议使用受广义互助启发的损失函数。结果显示，带有5G标准LDPC代码的调制订单16和64的基于调制订单的基于常规正交幅度调制（QAM）的编码方案的表现均优于传统的正交幅度调制（QAM）。
We propose a hybrid coded modulation scheme which composes of inner and outer codes. The outer-code can be any standard binary linear code with efficient soft decoding capability (e.g. low-density parity-check (LDPC) codes). The inner code is designed using a deep neural network (DNN) which takes the channel coded bits and outputs modulated symbols. For training the DNN, we propose to use a loss function that is inspired by the generalized mutual information. The resulting constellations are shown to outperform the conventional quadrature amplitude modulation (QAM) based coding scheme for modulation order 16 and 64 with 5G standard LDPC codes.
