End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information

GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates. State-of-the-art constellations in 2D and 4D are found providing reach increases up to 26% w.r .t. to QAM. I NTRODUCTION S IGNAL shaping has recently received considerable attention in the literature and is now regarded as a key technique to improve throughput in high-speed fiberoptic systems. Shaping methods can be broadly categorized into probabilistic shaping (PS) and geometric shaping (GS), both having distinct advantages and disadvantages [1]-[3].