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Found 11 papers, 0 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.
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.
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.
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.
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-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.
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.
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.
