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Found 28 papers, 2 papers with code
BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling
The GCS scheme is experimentally demonstrated in a multi-span recirculating loop coherent optical fiber transmission system with a total distance of up to 3000 km.
Multi-parallel-task Time-delay Reservoir Computing combining a Silicon Microring with WDM
We numerically demonstrate a microring-based time-delay reservoir computing scheme that simultaneously solves three tasks involving time-series prediction, classification, and wireless channel equalization.
Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing
Microring resonators (MRRs) are promising devices for time-delay photonic reservoir computing, but the impact of the different physical effects taking place in the MRRs on the reservoir computing performance is yet to be fully understood.
Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers
End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems.
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.
Low-complexity Samples versus Symbols-based Neural Network Receiver for Channel Equalization
In this work, we propose a low-complexity NN that performs samples-to-symbol equalization, meaning that the NN-based equalizer includes match filtering and downsampling.
Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers.
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.
Impact of Free-carrier Nonlinearities on Silicon Microring-based Reservoir Computing
We quantify the impact of thermo-optic and free-carrier effects on time-delay reservoir computing using a silicon microring resonator.
Data-Driven Modeling of Directly-Modulated Lasers
The end-to-end optimization of links based on directly-modulated lasers may require an analytically differentiable channel.
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning
We demonstrate transfer learning-assisted neural network models for optical matrix multipliers with scarce measurement data.
Reservoir Computing-based Multi-Symbol Equalization for PAM 4 Short-reach Transmission
We propose spectrum-sliced reservoir computer-based (RC) multi-symbol equalization for 32-GBd PAM4 transmission.
Data-driven Modeling of Mach-Zehnder Interferometer-based Optical Matrix Multipliers
Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for implementing matrix multiplications.
Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers
In this case, the experimental results assert that for 2D profiles with the target flat gain levels, the DE obtains less than 1 dB maximum gain deviation, when the setup is not physically limited in the pump power values.
Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model
The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps.
Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers
We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance.
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs
We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes.
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.
Inverse design of Raman amplifier in frequency and distance domain using Convolutional Neural Networks
We present a Convolutional Neural Network (CNN) architecture for inverse Raman amplifier design.
Gradient-free training of autoencoders for non-differentiable communication channels
Our results indicate that the autoencoder can be successfully optimized using the proposed training method to achieve better robustness to residual phase noise with respect to standard constellation schemes such as Quadrature Amplitude Modulation and Iterative Polar Modulation for the considered conditions.
Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning
A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated.
Applied Physics Optics
Experimental Demonstration of Optoelectronic Equalization for Short-reach Transmission with Reservoir Computing
A receiver with shared complexity between optical and digital domains is experimentally demonstrated.
End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theory
In this first numerical analysis, the detection is performed by a neural network (NN), whereas the symbol-to-waveform mapping is aided by the nonlinear Fourier transform (NFT) theory in order to simplify and guide the optimization on the modulation side.
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.
An Overview on Application of Machine Learning Techniques in Optical Networks
Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data.
