Mitigation of Polarization-Induced Fading in Optical Vector Network Analyzer for the Characterization of km-scale Space-Division Multiplexing Fibers

We propose an optimized optical vector network analyzer with automatic polarization control to stabilize the reference arm polarization throughout the sweep range. We demonstrate this technique, successfully removing the polarization-induced fading and measurement distortions in insertion loss by characterizing a 10 km multi-core fiber.

Co-propagation of Classical and Continuous-variable QKD Signals over a Turbulent Optical Channel with a Real-time QKD Receiver

We demonstrate classical and quantum signal co-propagation over a turbulent free-space channel with 3 Tbit/s throughput and record 2.7 Mbit/s secret-key rate. Our real-time GPU-based receiver assessed quantum signal integrity under different turbulence scenarios for the first time.

Adaptive Reconciliation for Experimental Continuous-Variable Quantum Key Distribution Over a Turbulent Free-Space Optical Channel

We experimentally demonstrate adaptive reconciliation for continuous-variable quantum key distribution over a turbulent free-space optical channel. Additionally, we propose a method for optimising the reconciliation efficiency, increasing secret key rates by up to 8.1%.

Experimental Validation of Sequence-Wise Predistorter for Evaluation of Geometrically Shaped 128-QAM

A predistorter for transmitter nonlinearities is applied to the evaluation of a geometrically shaped constellation, such that constellation points are transmitted correctly during the evaluation of the geometrically shaped constellation.

MDG and SNR Estimation in SDM Transmission Based on Artificial Neural Networks

The increase in capacity provided by coupled SDM systems is fundamentally limited by MDG and ASE noise. Therefore, monitoring MDG and optical SNR is essential for accurate performance evaluation and troubleshooting. Recent works show that the conventional MDG estimation method based on the transfer matrix of MIMO equalizers optimizing the MMSE underestimates the actual value at low SNR. Besides, estimating the optical SNR itself is not a trivial task in SDM systems, as MDG strongly influences the electrical SNR after the equalizer. In a recent work we propose an MDG and SNR estimation method using ANN. The proposed ANN-based method processes features extracted at the receiver after DSP. In this paper, we discuss the ANN-based method in detail, and validate it in an experimental 73-km 3-mode transmission link with controlled MDG and SNR. After validation, we apply the method in a case study consisting of an experimental long-haul 6-mode link. The results show that the ANN estimates both MDG and SNR with high accuracy, outperforming conventional methods.

Kramers-Kronig Receiver Combined With Digital Resolution Enhancer

A Kramers-Kronig receiver with a continuous wave tone added digitally at the transmitter is combined with a digital resolution enhancer to limit the increase in transmitter quantization noise. Performance increase is demonstrated, as well as the ability to reduce the number of bits in the digital-to-analog converter.

Neural-network-based MDG and Optical SNR Estimation in SDM Transmission

We propose a neural network model for MDG and optical SNR estimation in SDM transmission. We show that the proposed neural-network-based solution estimates MDG and SNR with high accuracy and low complexity from features extracted after DSP.