16-QAM Probabilistic Constellation Shaping by Learning the Distribution of Transmitted Symbols from the Training Sequence

A technique for probabilistic constellation shaping based on distribution learning from a training sequence is investigated. In this approach, the probability distribution is optimized such that it can maximize the mutual information. The effectiveness of this approach is verified by shaping 10 Gbaud 16QAM in simulation and experiment.