Kramers-Kronig Receiver With Digitally Added Carrier Combined With Digital Resolution Enhancer

Hout, Menno van den; Heide, Sjoerd van der; Okonkwo, Chigo

doi:10.1109/jlt.2022.3142353

Cited by 6 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (8), the term N FFT − N CD + 1 denotes the number of output samples produced by each iteration of the overlap-save algorithm. The optimum value for C DBP is obtained for the N FFT value that minimizes (8).…”

Section: A Complexity Of Rxmentioning

confidence: 99%

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)

View full text Add to dashboard Cite

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.

show abstract

Section: A Complexity Of Rxmentioning

confidence: 99%

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)

View full text Add to dashboard Cite

show abstract

“…Furthermore, a higher CSPR increases the transmitter requirements. For example, the required resolution of the digital-to-analog converter (DAC) increases with the CSPR in those systems where the carrier is digitally generated (virtual tone) together with the information-bearing signal [7], [8]. Therefore, the CSPR is a key parameter to be optimized for system performance, and the lowest possible CSPR is preferred.…”

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

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.

show abstract

“…The KK receiver has been shown to offer improved SSBI cancellation performance compared to other iterative phase retrieval schemes [10], and has been widely investigated since its introduction by Mecozzi et al [11]. Yet, the KK receiver requires a carrier-to-signal-power ratio (CSPR) higher than 6 dB to achieve the minimum-phase condition, which enhances the impact of nonlinear fiber propagation effects, and increases the requirements of the digital-to-analog converter (DAC) at the transmitter side [12]. Therefore, there is significant interest in developing novel phase retrieval schemes that can achieve phase retrieval with low steup complexity and at low CSPR.…”

mentioning

confidence: 99%

Edge-Carrier-Assisted Phase Retrieval at Low-CSPR and Low-Dispersion Diversity With Deep Learning

Orsuti,

Santagiustina,

Galtarossa

et al. 2024

J. Lightwave Technol.

View full text Add to dashboard Cite

We investigate the performance of deep learning in recovering the complex-valued field of a weak-carrier-assisted single side-band signal from two intensity measurements that are decorrelated by dispersion. The proposed scheme relies on a supervised learning-based convolutional neural network to map the intensity measurements to the full-field. Unlike conventional iterative phase retrieval schemes, the proposed scheme does not require any iterations, digital upsampling, or pilot symbols, and can operate both at low carrier-to-signal-power ratio (CSPR) and at low applied dispersion value. Through numerical simulations in relevant system settings, we compare the performance of the proposed scheme with two recently proposed carrier-assisted iterative phase retrieval schemes: one based on the solution of a nonlinear optimization problem, and the other based on a modified Gerchberg-Saxton algorithm. The results show that the proposed scheme complies with the 7% hard-decision forward error correction threshold after 24 GBaud 32-QAM transmission over 100 km of standard single-mode fiber at a CSPR of 0 dB, with 3.6 times lower applied dispersion value, 30% to 90% lower complexity, and with less than 2 dB sensitivity penalty compared to conventional iterative phase retrieval schemes. These results support the potential of deep learning to realize phase retrieval-based coherent receivers that are compatible with the low complexity requirements of short-reach optical networks.

show abstract

Kramers-Kronig Receiver With Digitally Added Carrier Combined With Digital Resolution Enhancer

Cited by 6 publications

References 20 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

Edge-Carrier-Assisted Phase Retrieval at Low-CSPR and Low-Dispersion Diversity With Deep Learning

Contact Info

Product

Resources

About