Geometrically Shaped Multi-Dimensional Modulation Formats Designed by Deep Learning

Abstract

Geometrically shaped multi-dimensional modulation formats are designed by a bit-wise autoencoder (AE), which is one of the deep learning applications. The optimized four-dimensional (4-D) modulation constellation diagrams in two-dimensional (2-D) projection have highly unique symmetrical constellations with clever labeling. In addition, the numerically evaluated BER performances show that the receiver sensitivities of the optimally shaped multi-dimensional modulation format are equivalent to or better than those of the conventional 4-D and 2-D formats with the same transmission capacity.