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Found 14 papers, 1 papers with code
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.
Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication
Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication.
Efficient Training of Volterra Series-Based Pre-distortion Filter Using Neural Networks
We present a simple, efficient "direct learning" approach to train Volterra series-based digital pre-distortion filters using neural networks.
End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping
We present a novel autoencoder-based learning of joint geometric and probabilistic constellation shaping for coded-modulation systems.
Deep Neural Network-aided Soft-Demapping in Optical Coherent Systems: Regression versus Classification
We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in coherent optical communications, where the transmission channel is nonlinear and dispersive.
On the Comparison of Single-Carrier vs. Digital Multi-Carrier Signaling for Long-Haul Transmission of Probabilistically Shaped Constellation Formats
We report on theoretical and experimental investigations of the nonlinear tolerance of single carrier and digital multicarrier approaches with probabilistically shaped constellations.
Single-ended Coherent Receiver
One potential approach for solving this problem is to leverage the concept of single-ended coherent receiver (SER) where single-ended PDs are used instead of the balanced PDs.
End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model
We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model.
Experimental Investigation of Deep Learning for Digital Signal Processing in Short Reach Optical Fiber Communications
The investigation of digital signal processing (DSP) optimized on experimental data is extended to pulse amplitude modulation with receivers performing sliding window sequence estimation using a feed-forward or a recurrent neural network as well as classical nonlinear Volterra equalization.
Optical Fiber Communication Systems Based on End-to-End Deep Learning
We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning.
Concept and Experimental Demonstration of Optical IM/DD End-to-End System Optimization using a Generative Model
We perform an experimental end-to-end transceiver optimization via deep learning using a generative adversarial network to approximate the test-bed channel.
Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing
In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IM/DD) links.
Information Theory Signal Processing Information Theory
A Compressed Sensing Approach for Distribution Matching
In this work, we formulate the fixed-length distribution matching as a Bayesian inference problem.
Control and Detection of Discrete Spectral Amplitudes in Nonlinear Fourier Spectrum
To generate an $N$-solitary waveform from desired discrete spectrum (eigenvalue and discrete spectral amplitudes), we use the Darboux Transform.
Numerical Analysis Optics
