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Efficient Training of Volterra Series-Based Pre-Distortion Filter Using Neural Networks

descriptionPublicationkeyboard_double_arrow_right Article , Presentation , Preprint 01 Jan 2022Embargo end date: 01 Jan 2021Publisher:Optica Publishing GroupJournal:Optical Fiber Communication Conference (OFC) 2022Funded by:EC | FONTE

Authors: Bajaj, Vinod; Chagnon, Mathieu; Wahls, Sander; Aref, Vahid;

doi: 10.1364/ofc.2022.m1h.3 , 10.5281/zenodo.6335504 , 10.48550/arxiv.2112.06637 , 10.5281/zenodo.6335505

arXiv: http://arxiv.org/abs/2112.06637

Efficient Training of Volterra Series-Based Pre-Distortion Filter Using Neural Networks

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Abstract

We present a simple, efficient “direct learning” approach to train Volterra series-based digital pre-distortion filters using neural networks. We show its superior performance over conventional training methods using a 64-QAM 64 GBaud simulated transmitter with varying transmitter nonlinearity and noisy conditions.

Related Organizations

Delft University of Technology
Netherlands
Delft University of Technology
Netherlands

Keywords

Signal Processing (eess.SP), FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing

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