Optimization of Recurrent Neural Network-Based Pre-Distorter for Coherent Optical Transmitter via Stochastic Orthogonal Decomposition

Moshe Tzur; Gil Paryanti; Dan Sadot

doi:10.1364/SPPCOM.2020.SpTh2I.5

OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)
OSA Technical Digest (Optica Publishing Group, 2020),
paper SpTh2I.5
•https://doi.org/10.1364/SPPCOM.2020.SpTh2I.5

Optimization of Recurrent Neural Network-Based Pre-Distorter for Coherent Optical Transmitter via Stochastic Orthogonal Decomposition

Moshe Tzur, Gil Paryanti, and Dan Sadot

Signal Processing in Photonic Communications 2020

Washington, DC United States
13–16 July 2020
ISBN: 978-1-943580-79-8

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Signal Processing II (SpTh2I)

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M. Tzur, G. Paryanti, and D. Sadot, "Optimization of Recurrent Neural Network-Based Pre-Distorter for Coherent Optical Transmitter via Stochastic Orthogonal Decomposition," in OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF), L. Caspani, A. Tauke-Pedretti, F. Leo, and B. Yang, eds., OSA Technical Digest (Optica Publishing Group, 2020), paper SpTh2I.5.

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Abstract

A gradual training paradigm for Recurrent Neural Network-based pre-distorter is proposed. Stochastic decomposition is used to separate nonlinearity and quantization noise features. Performance improvement of more than 6dB is presented.

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