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

We propose a deep learning-based phase retrieval method to accurately reconstruct the optical field of a single-sideband 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

DAS Over Multimode Fibers With Reduced Fading by Coherent Averaging of Spatial Modes

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

Edge-carrier-assisted Phase-Retrieval Based on Deep Learning Enabling low CSPR and low Applied Dispersion Values

We explore the use of deep learning to loosen the constraints and enhance the performance of weak-carrier-assisted phase-retrieval receivers. The applied-dispersion-value can be reduced by 4-times and the complexity by 50% with low sensitivity penalties

Coherent Combination Method applied to Distributed Acoustic Sensing over Deployed Multicore Fiber

From Distributed Acoustic Sensing (DAS) measurements over deployed Multi-Core Fiber (MCF), we discuss several signal processing options to enhance the sensing sensitivity, namely core combination and longitudinal averaging

Phase Retrieval Receiver Based on Deep Learning for Minimum-phase Signal Recovery

We propose a deep learning-based phase retrieval receiver for minimum-phase signal recovery. Simulation results show that the HD-FEC limit at BER 3.8e-3 is achieved with 2-dB lower CSPR and 1.6-dB better receiver sensitivity compared to a conventional four-fold upsampled Kramers-Kronig receiver in relevant system settings

Experimental demonstration of a multi-core fiber seeded comb optical network (MCF-SCON)

We propose and experimentally demonstrate an optical network architecture that uses wideband optical frequency comb (OFC) sources synchronized with transmitted network broadcast seed signals. All network nodes are connected by multi-core fibers (MCFs) which are used for data transmission and seed distribution. The nodes that connect the data transmission and seed distribution layers contain an OFC that may be operated in master or slave mode to provide carriers both for modulation and local oscillators for coherent detection. Nodes are able to switch, amplify, split, and regenerate the seed lightwave for full network distribution over large areas. Using OFCs with 25 GHz spacing covering the C- and L-bands, we experimentally investigate a range of scenarios compatible with metro or inter-data-center network architectures. These scenarios include single span transmission, bi-directional transmission, and multi-hop seed and data transmission including seed amplification, splitting, and regeneration with laser injection locking within network nodes, and a maximum transmission distance of 130 km. We show that MCF seed and data distribution with spatial-switching enables identical network-wide transceiver combs with the potential for reducing the complexity of digital signal processing as well as significant hardware sharing. Data-rates of approximately 100 Tb/s/core and 600 Tb/s per fiber are measured showing the potential of this architecture for high-capacity networking with OFCs replacing hundreds of transmitter and local oscillator lasers in each node.</p

Wideband S, C, + L-Band Comb Regeneration in Large-Scale Few-Mode MCF Link with Single-Mode Seed Channel

We use wideband optical frequency combs covering 134 nm to transmit &gt;336 Tb/s in a single core of a multi-core, 3-mode fiber with a single-mode seed transmission core to regenerate synchronized local comb enabling simplified coherent reception