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Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers-Kronig receiver

Sarah Masaad (UGent) , Emmanuel Gooskens (UGent) , Stijn Sackesyn (UGent) , Joni Dambre (UGent) and Peter Bienstman (UGent)
(2023) NANOPHOTONICS. 12(5). p.925-935
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Abstract
Photonic reservoirs are machine learning based systems that boast energy efficiency and speediness. Thus they can be deployed as optical processors in fiber communication systems to aid or replace digital signal equalization. In this paper, we simulate the use of a passive photonic reservoir to target nonlinearity-induced errors originating from self-phase modulation in the fiber and from the nonlinear response of the modulator. A 64-level quadrature-amplitude modulated signal is directly detected using the recently proposed Kramers-Kronig (KK) receiver. We train the readout weights by backpropagating through the receiver pipeline, thereby providing extra nonlinearity. Statistically computed bit error rates for fiber lengths of up to 100 km fall below 1 x 10(-3) bit error rate, outperforming an optical feed-forward equalizer as a linear benchmark. This can find applications in inter-datacenter communications that benefit from the hardware simplicity of a KK receiver and the low power and low latency processing of a photonic reservoir.
Keywords
Kramers-Kronig receiver, nonlinearity mitigation, photonic reservoir, computing

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MLA
Masaad, Sarah, et al. “Photonic Reservoir Computing for Nonlinear Equalization of 64-QAM Signals with a Kramers-Kronig Receiver.” NANOPHOTONICS, vol. 12, no. 5, 2023, pp. 925–35, doi:10.1515/nanoph-2022-0426.
APA
Masaad, S., Gooskens, E., Sackesyn, S., Dambre, J., & Bienstman, P. (2023). Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers-Kronig receiver. NANOPHOTONICS, 12(5), 925–935. https://doi.org/10.1515/nanoph-2022-0426
Chicago author-date
Masaad, Sarah, Emmanuel Gooskens, Stijn Sackesyn, Joni Dambre, and Peter Bienstman. 2023. “Photonic Reservoir Computing for Nonlinear Equalization of 64-QAM Signals with a Kramers-Kronig Receiver.” NANOPHOTONICS 12 (5): 925–35. https://doi.org/10.1515/nanoph-2022-0426.
Chicago author-date (all authors)
Masaad, Sarah, Emmanuel Gooskens, Stijn Sackesyn, Joni Dambre, and Peter Bienstman. 2023. “Photonic Reservoir Computing for Nonlinear Equalization of 64-QAM Signals with a Kramers-Kronig Receiver.” NANOPHOTONICS 12 (5): 925–935. doi:10.1515/nanoph-2022-0426.
Vancouver
1.
Masaad S, Gooskens E, Sackesyn S, Dambre J, Bienstman P. Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers-Kronig receiver. NANOPHOTONICS. 2023;12(5):925–35.
IEEE
[1]
S. Masaad, E. Gooskens, S. Sackesyn, J. Dambre, and P. Bienstman, “Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers-Kronig receiver,” NANOPHOTONICS, vol. 12, no. 5, pp. 925–935, 2023.
@article{01GR1JJ1QQVJGA38R2B4YJ5JH0,
  abstract     = {{Photonic reservoirs are machine learning based systems that boast energy efficiency and speediness. Thus they can be deployed as optical processors in fiber communication systems to aid or replace digital signal equalization. In this paper, we simulate the use of a passive photonic reservoir to target nonlinearity-induced errors originating from self-phase modulation in the fiber and from the nonlinear response of the modulator. A 64-level quadrature-amplitude modulated signal is directly detected using the recently proposed Kramers-Kronig (KK) receiver. We train the readout weights by backpropagating through the receiver pipeline, thereby providing extra nonlinearity. Statistically computed bit error rates for fiber lengths of up to 100 km fall below 1 x 10(-3) bit error rate, outperforming an optical feed-forward equalizer as a linear benchmark. This can find applications in inter-datacenter communications that benefit from the hardware simplicity of a KK receiver and the low power and low latency processing of a photonic reservoir.}},
  author       = {{Masaad, Sarah and Gooskens, Emmanuel and Sackesyn, Stijn and Dambre, Joni and Bienstman, Peter}},
  issn         = {{2192-8606}},
  journal      = {{NANOPHOTONICS}},
  keywords     = {{Kramers-Kronig receiver,nonlinearity mitigation,photonic reservoir,computing}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{925--935}},
  title        = {{Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers-Kronig receiver}},
  url          = {{http://doi.org/10.1515/nanoph-2022-0426}},
  volume       = {{12}},
  year         = {{2023}},
}

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