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.
We demonstrate a successful field trial of MIMO-DAS over multicore fiber (MCF) allowing for accurate localization of acoustic events in the city of L’Aquila, Italy. We show a 2m spatial resolution and 1mHz-380Hz acoustic bandwidth.
The performance of a distributed acoustic sensor based on heterodyne OTDR applied to multimode fibers is analyzed. Substantial reduction of the noise floor is achieved by exploiting spatial diversity and a novel aggregation algorithm.
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.
We investigate the performance of distributed acoustic sensing over multi-mode fibers based on heterodyne phase-sensitive optical time-domain reflectometry. We report a mathematical model describing the relation between phase variation and applied strain in the presence of multi-mode propagation that supports the feasibility of distributed acoustic measurements over multi-mode fibers. We also propose a novel coherent averaging method that achieves up to a three-fold reduction of the noise floor compared to state-of-the-art methods.
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