论文标题
基于模型的关节概率和几何形状的深度学习光学通信
Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication
论文作者
论文摘要
基于自动编码器的深度学习可用于共同优化光学相干通信的几何和概率星座。优化的星座的塑造优于256个QAM Maxwell-Boltzmann概率分布，具有额外的0.05位/4D-Symbol共同信息，用于170 km SMF链接的64 GBD传输。
Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.
