A Study on Sampling Penalties Reduction of Kramers-Kronig Receivers

Toba, Kentaro; Fujita, T.; Tsukui, Enzo; Sampath, K. I. Amila; Maeda, Joji

doi:10.1109/jlt.2021.3099938

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The second is to replace the square-root function with a logarithmic function, to allow analog processing using semiconductor diodes [11]. Alternatively, in [12], the sampling rate and CSPR requirements are relaxed by combining digital upsampling with harmonic filtering. Two low-pass filters are inserted after each nonlinear operation in the KK algorithm to eliminate out-of-band harmonics before downsampling.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Phase Retrieval Scheme for Minimum-Phase Signal Recovery

Orsuti

Antonelli

Chiuso

et al. 2023

J. Lightwave Technol.

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We propose a deep learning-based phase retrieval method to accurately reconstruct the optical field of a singlesideband minimum-phase signal from the directly detected intensity waveform. Our method relies on a fully convolutional Neural Network (NN) model to realize non-iterative and robust phase retrieval. The NN is trained so that it performs fullfield reconstruction and jointly compensates for transmission impairments. Compared to the recently proposed Kramers-Kronig (KK) receiver, our method avoids the distortions introduced by the nonlinear operations involved in the KK phase-retrieval algorithm and hence does not require digital upsampling. We validate the proposed phase-retrieval method by means of extensive numerical simulations in relevant system settings, and we compare the performance of the proposed scheme with the conventional KK receiver operated with a 4-fold digital upsampling. The results show that the 7% hard-decision forward error correction (HD-FEC) threshold at BER 3.8e-3 can be achieved with up to 2.8 dB lower carrier-to-signal power ratio (CSPR) value and 1.8 dB better receiver sensitivity compared to the conventional 4-fold upsampled KK receiver. We also present a comparative analysis of the complexity of the proposed scheme with that of the KK receiver, showing that the proposed scheme can achieve the 7% HD-FEC threshold with 1.6 dB lower CSPR, 0.4 dB better receiver sensitivity, and 36% lower complexity.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Phase Retrieval Scheme for Minimum-Phase Signal Recovery

Orsuti

Antonelli

Chiuso

et al. 2023

J. Lightwave Technol.

View full text Add to dashboard Cite

show abstract

Deep learning-based Phase Retrieval Scheme for Minimum Phase Signal Recovery

Orsuti

Antonelli

Chiuso

et al. 2022

2022 Italian Conference on Optics and Photonics (ICOP)

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We propose a deep learning-based phase retrieval method to accurately reconstruct the optical field of a singlesideband minimum-phase signal from the directly detected intensity waveform. Our method relies on a fully convolutional Neural Network (NN) model to realize non-iterative and robust phase retrieval. The NN is trained so that it performs full-field reconstruction and jointly compensates for transmission impairments. Compared to the recently proposed Kramers-Kronig (KK) receiver, our method avoids the distortions introduced by the nonlinear operations involved in the KK phase-retrieval algorithm and hence does not require digital upsampling. We validate the proposed phase-retrieval method by means of extensive numerical simulations in relevant system settings, and we compare the performance of the proposed scheme with the conventional KK receiver operated with a 4-fold digital upsampling. The results show that the 7% hard-decision forward error correction (HD-FEC) threshold at BER 3.8e-3 can be achieved with up to 2.8 dB lower carrier-to-signal power ratio (CSPR) value and 1.8 dB better receiver sensitivity compared to the conventional 4-fold upsampled KK receiver. We also present a comparative analysis of the complexity of the proposed scheme with that of the KK receiver, showing that the proposed scheme can achieve the 7% HD-FEC threshold with 1.6 dB lower CSPR, 0.4 dB better receiver sensitivity, and 36% lower complexity.

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An Improved Kramers-Kronig Receiver With Upsampling-Free and Low Carrier-to-Signal Power Ratio

Luo,

Li,

et al. 2024

J. Lightwave Technol.

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A Study on Sampling Penalties Reduction of Kramers-Kronig Receivers

Cited by 3 publications

References 16 publications

Deep Learning-Based Phase Retrieval Scheme for Minimum-Phase Signal Recovery

Deep Learning-Based Phase Retrieval Scheme for Minimum-Phase Signal Recovery

Deep learning-based Phase Retrieval Scheme for Minimum Phase Signal Recovery

An Improved Kramers-Kronig Receiver With Upsampling-Free and Low Carrier-to-Signal Power Ratio

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