End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
Kadir Gümüş, Alex Alvarado, Bin Chen, Christian Häger, Erik Agrell
Abstract
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.
Topics & Concepts
End-to-end principleConstellationGradient descentComputer scienceMutual informationAlgorithmMathematicsArtificial intelligencePhysicsArtificial neural networkAstronomySparse and Compressive Sensing TechniquesAdvanced Image and Video Retrieval TechniquesNeural Networks and Reservoir Computing