Open AccessExperimental realization of integrated photonic reservoir computing for nonlinear fiber distortion compensation
Author(s) -
Stijn Sackesyn,
Chonghuai Ma,
Joni Dambre,
Peter Bienstman
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.435013
Subject(s) - digital signal processing , photonics , computer science , reservoir computing , electronic engineering , signal processing , distortion (music) , digital signal processor , nonlinear system , chip , compensation (psychology) , realization (probability) , optical fiber , computer hardware , optics , telecommunications , engineering , physics , artificial neural network , psychoanalysis , psychology , amplifier , statistics , mathematics , bandwidth (computing) , quantum mechanics , machine learning , recurrent neural network
Nonlinearity mitigation in optical fiber networks is typically handled by electronic Digital Signal Processing (DSP) chips. Such DSP chips are costly, power-hungry and can introduce high latencies. Therefore, optical techniques are investigated which are more efficient in both power consumption and processing cost. One such a machine learning technique is optical reservoir computing, in which a photonic chip can be trained on certain tasks, with the potential advantages of higher speed, reduced power consumption and lower latency compared to its electronic counterparts. In this paper, experimental results are presented where nonlinear distortions in a 32 GBPS OOK signal are mitigated to below the 0.2 × 10 -3 FEC limit using a photonic reservoir. Furthermore, the results of the reservoir chip are compared to a tapped delay line filter to clearly show that the system performs nonlinear equalisation.