Neural Network Assisted Geometric Shaping for 800Gbit/s and 1Tbit/s Optical Transmission
Maximilian Schaedler, Stefano Calabrò, Fabio Pittalà, Georg Böcherer, Maxim Kuschnerov, Christian Bluemm, Stephan Pachnicke
Abstract
End-to-end learning for amplified and unamplified links including binary mapping is proposed to improve the performance of optical coherent systems. 1.0dB and 1.2dB gains are demonstrated on coherent 92GbaudDP-32QAM 800Gb/s and 82GbaudDP- 128QAM 1Tb/s measurements, respectively.
Topics & Concepts
Computer scienceTransmission (telecommunications)Artificial neural networkOptical communicationBinary numberOptical performance monitoringOpticsElectronic engineeringPhysicsTelecommunicationsArtificial intelligenceWavelength-division multiplexingMathematicsArithmeticEngineeringWavelengthOptical Network TechnologiesAdvanced Photonic Communication SystemsNeural Networks and Reservoir Computing