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Found 11 papers, 4 papers with code
Optimization of Raman amplifiers: a comparison between black-, grey- and white-box modeling
Designing and optimizing optical amplifiers to maximize system performance is becoming increasingly important as optical communication systems strive to increase throughput.
Rate Adaptive Geometric Constellation Shaping Using Autoencoders and Many-To-One Mapping
A many-to-one mapping geometric constellation shaping scheme is proposed with a fixed modulation format, fixed FEC engine and rate adaptation with an arbitrarily small step.
Spectral Power Profile Optimization of Field-Deployed WDM Network by Remote Link Modeling
A digital twin model of a multi-node WDM network is obtained from a single access point.
Capacity and Achievable Rates of Fading Few-mode MIMO IM/DD Optical Fiber Channels
The optical fiber multiple-input multiple-output (MIMO) channel with intensity modulation and direct detection (IM/DD) per spatial path is treated.
End-to-end Learning of a Constellation Shape Robust to Channel Condition Uncertainties
Two noise models are considered for the additive noise: white Gaussian noise and nonlinear interference noise model for fiber nonlinearities.
End-to-end Learning of a Constellation Shape Robust to Variations in SNR and Laser Linewidth
We propose an autoencoder-based geometric shaping that learns a constellation robust to SNR and laser linewidth estimation errors.
Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
Cascades of a machine learning-based EDFA gain model trained on a single physical device and a fully differentiable stimulated Raman scattering fiber model are used to predict and optimize the power profile at the output of an experimental multi-span fully-loaded C-band optical communication system.
Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built from experimental measurements.
End-to-end Learning for GMI Optimized Geometric Constellation Shape
Autoencoder-based geometric shaping is proposed that includes optimizing bit mappings.
Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning
In this paper, an unsupervised machine learning method for geometric constellation shaping is investigated.
Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities
A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design.
