Combining nonlinear Fourier transform and neural network-based processing in optical communications
Oleksandr Kotlyar, Maryna Pankratova, Morteza Kamalian-Kopae, Anastasiia Vasylchenkova, Jaroslaw E. Prilepsky, Sergei K. Turitsyn
Abstract
We propose a method to improve the performance of the nonlinear Fourier transform (NFT)-based optical transmission system by applying the neural network post-processing of the nonlinear spectrum at the receiver. We demonstrate through numerical modeling about one order of magnitude bit error rate improvement and compare this method with machine learning processing based on the classification of the received symbols. The proposed approach also offers a way to improve numerical accuracy of the inverse NFT; therefore, it can find a range of applications beyond optical communications.
Topics & Concepts
Computer scienceArtificial neural networkFourier transformNonlinear systemSignal processingTransmission (telecommunications)Bit error rateOpticsFast Fourier transformInverseRange (aeronautics)AlgorithmOptical communicationArtificial intelligenceElectronic engineeringTelecommunicationsMathematicsPhysicsEngineeringRadarQuantum mechanicsDecoding methodsGeometryAerospace engineeringMathematical analysisOptical Network TechnologiesAdvanced Photonic Communication SystemsAdvanced Fiber Laser Technologies