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End-to-End Deep Learning of Optical Fiber Communications

https://doi.org/10.1109/jlt.2018.2865109

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Compensation of Dispersion and Nonlinear Impairments Using Digital Backpropagation

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Capacity Limits of Optical Fiber Networks

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Machine Learning Techniques in Optical Communication

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Artificial Neural Network Nonlinear Equalizer for Coherent Optical OFDM

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Machine Learning Paradigms for Next-Generation Wireless Networks

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Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks

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Learning to Modulate for Non-coherent MIMO

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Deep Learning-Based Joint Channel Coding and Frequency Modulation for Low Power Connectivity

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Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning

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Auto-encoder Based Analog Network Coding for Free-Space Optical Two-Way Communications

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A Transfer Learning Based Deep Model for Electrical Load Prediction

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Data-Aided Learning for Multi-Antenna OFDM Systems with Superimposed Pilot in Time-Varying Channel

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A RNN Decoder for Channel Decoding under Correlated Noise

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Deep Learning Based Constellation Rearrangement Design in Hybrid ARQ System

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Learning to Design Constellation of MLC With Geometric Shaping

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Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning

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Autoencoder-Based Resnet Block for Laser Phase Noise in Optical Communications

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A Fiber-Optic Channel Modeled Through BiLSTM Technique

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An Improved End-to-end Optical Transmission System Based On Deep Learning

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Adaptive Channel Modeling with Conditional Generative Adversarial Network for 209-GHz Fiber-Terahertz Integrated Communication System

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End-to-End Learning of Noise Shaping for OAM Mode Division Multiplexing Transmission

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Nonlinear Impairment Mitigation Based on Full-Link Physically Interpretable E2E Optimization Framework for Over 300-Gbps IM/DD Transmission

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End-to-end Deep Learning for VCSEL’s Nonlinear Digital Pre-Distortion

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Flexible Autoencoder for Orthogonal Chirp Division Multiplexing Communications

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Neural Networks for PAPR Reduction in Optical OFDM Signal Transmission

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On the Use of Feedforward Neural Networks to Improve the Intercore Crosstalk Tolerance in Self-Coherent MCF Systems

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Machine Learning for Simplified Future Passive Optical Networks

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An End-to-end Learning Framework for Joint Compensation of Impairments in Coherent Optical Communication Systems

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Optical Fiber Communication Systems Based on End-to-End Deep Learning : (Invited Paper)

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A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-Implementation

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End-to-End Learning System for Symbol Decoding in Optical Communication

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Simulation Platform for End-to-End Deep Learning Based Coherent Fiber Communication System

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Optimizing Optical Signal Quality with Deep Learning Dispersion Compensation at Various Distances

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Deep Neural Network-Based Digital Twin Modeling Method for All-Optical Matching System

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Applying Deep Residual Network to Suppress Optical Coding Access Interference

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QoT Estimation of a Commercial DWDM Transmission System Using Artificial Neural Network and Its Feature Importance Analysis

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Control of the modal composition of the microstructured fiber to determine the optimum parameters of the process of its production cycle

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An Optical Communication's Perspective on Machine Learning and Its Applications

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Optical Communications for Short Reach

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Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach

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Probabilistically Coded Modulation Formats for 5G Mobile Fronthaul Networks

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Machine Learning Regression Approach to the Nanophotonic Waveguide Analyses

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Low Complexity OSNR Monitoring and Modulation Format Identification Based on Binarized Neural Networks

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Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation

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A Direct Learning Approach for Neural Network Based Pre-Distortion for Coherent Nonlinear Optical Transmitter

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Performance Enhancement of Optical Comb Based Microwave Photonic Filter by Machine Learning Technique

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AdaNN: Adaptive Neural Network-Based Equalizer via Online Semi-Supervised Learning

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Data-driven Optical Fiber Channel Modeling: A Deep Learning Approach

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Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks

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Predicting Kerr Soliton Combs in Microresonators via Deep Neural Networks

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Feedforward and Recurrent Neural Network-Based Transfer Learning for Nonlinear Equalization in Short-Reach Optical Links

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End-to-End Optimization of Coherent Optical Communications Over the Split-Step Fourier Method Guided by the Nonlinear Fourier Transform Theory

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Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

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Model-Aware Deep Learning Method for Raman Amplification in Few-Mode Fibers

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Analysis of Deep Neural Network Models for Inverse Design of Silicon Photonic Grating Coupler

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An Interpretable Mapping From a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization

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Gradient-Free Training of Autoencoders for Non-Differentiable Communication Channels

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Direct Detection Under Tukey Signalling

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Low-Complexity Multi-Task Learning Aided Neural Networks for Equalization in Short-Reach Optical Interconnects

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Fixed-Point Analysis and FPGA Implementation of Deep Neural Network Based Equalizers for High-Speed PON

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Neural-Network-Based Nonlinear Tomlinson-Harashima Precoding for Bandwidth-Limited Underwater Visible Light Communication

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Digital Longitudinal Monitoring of Optical Fiber Communication Link

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Principle-Driven Fiber Transmission Model Based on PINN Neural Network

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Machine-Learning-Aided Optical OFDM for Intensity Modulated Direct Detection

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End-to-End Deep Learning for Long-haul Fiber Transmission Using Differentiable Surrogate Channel

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Achievable Rates for Short-Reach Fiber-Optic Channels With Direct Detection

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End-to-End Learning of a Constellation Shape Robust to Channel Condition Uncertainties

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Geometric Constellation Shaping for Concatenated Two-Level Multi-Level Codes

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Deep Learning-Based Channel Modeling for Free Space Optical Communications

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A Multi-Rate Approach for Nonlinear Pre-Distortion Using End-to-End Deep Learning in IM-DD Systems

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Deep Learning-Based Phase Retrieval Scheme for Minimum-Phase Signal Recovery

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Waveform-to-Waveform End-to-End Learning Framework in a Seamless Fiber-Terahertz Integrated Communication System

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End-to-End Learning for VCSEL-Based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities

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Artificial Neural Network Assisted Probabilistic and Geometric Shaping for Flexible Rate High-Speed PONs

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End-to-End Optimization of Constellation Shaping for Wiener Phase Noise Channels With a Differentiable Blind Phase Search

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Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning

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A Data-Driven Digital Demodulator Based on Deep Learning for Radio Over Fiber Transmission System

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