Christian Häger
Visar 87 publikationer
A Physics-Informed Neural Network Approach to Modeling Dielectric Response Data
Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
Pilot-Free VCSEL Temperature Monitoring via Statistical Complexity
PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation
End-to-End Learning for RIS Profile Design and Channel Parameter Estimation under Pixel Failures
Inverse Modeling of Dielectric Response in Time Domain Using Physics-Informed Neural Networks
Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
On Adaptive Zero Forcing for Integrated Polarization Sensing and Coherent Optical Communications
Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
Learning Gradient-Based Feed-Forward Equalizer for VCSELs
Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation
Real-Time Implementation of Machine-Learning DSP
Deep-Learning-Based Channel Estimation for Distributed MIMO with 1-bit Radio-Over-Fiber Fronthaul
Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
Decoding Quantum LDPC Codes Using Graph Neural Networks
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Spatial Signal Design for Positioning via End-to-End Learning
FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
Model-Driven End-to-End Learning for Integrated Sensing and Communication
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Physics-Informed Neural Networks for Studying Charge Dynamics in Air
Improved Polarization Tracking in the Presence of PDL
FPGA-based Optical Kerr Effect Emulator
End-to-End Learning for Integrated Sensing and Communication
Machine learning for long-haul optical systems
Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
Data-Driven Estimation of Capacity Upper Bounds
Learning Optimal PAM Levels for VCSEL-based Optical Interconnects
Experimental Demonstration of Learned Pulse Shaping Filter for Superchannels
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Polarization Tracking in the Presence of PDL and Fast Temporal Drift
Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Pruning and Quantizing Neural Belief Propagation Decoders
Autoencoder-Based Unequal Error Protection Codes
Physics-Based Deep Learning for Fiber-Optic Communication Systems
Over-the-fiber Digital Predistortion Using Reinforcement Learning
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairments
Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
Learned Decimation for Neural Belief Propagation Decoders
Pruning Neural Belief Propagation Decoders
Decoding Reed-Muller Codes Using Redundant Code Constraints
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Learning Physical-Layer Communication with Quantized Feedback
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep learning
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
What Can Machine Learning Teach Us about Communications
On Low-Complexity Decoding of Product Codes for High-Throughput Fiber-Optic Systems
Nonlinear Interference Mitigation via Deep Neural Networks
Approaching Miscorrection-Free Performance of Product Codes with Anchor Decoding
Decoding Reed-Muller Codes Using Minimum- Weight Parity Checks
Miscorrection-free Decoding of Staircase Codes
Density Evolution and Error Floor Analysis for Staircase and Braided Codes
On the Information Loss of the Max-Log Approximation in BICM Systems
Analysis and Design of Spatially-Coupled Codes with Application to Fiber-Optical Communications
Deterministic and Ensemble-Based Spatially-Coupled Product Codes
A Deterministic Construction and Density Evolution Analysis for Generalized Product Codes
Density Evolution for Deterministic Generalized Product Codes with Higher-Order Modulation
Spatially-Coupled Codes for Optical Communications: State-of-the-Art and Open Problems
On Parameter Optimization for Staircase Codes
Optimized Bit Mappings for Spatially Coupled LDPC Codes over Parallel Binary Erasure Channels
Improving soft FEC performance for higher-order modulations via optimized bit channel mappings
On Signal Constellations and Coding for Long-Haul Fiber-Optical Systems
A Low-Complexity Detector for Memoryless Polarization-Multiplexed Fiber-Optical Channels
Design of APSK Constellations for Coherent Optical Channels with Nonlinear Phase Noise
Constellation Optimization for Coherent Optical Channels Distorted by Nonlinear Phase Noise
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Visar 4 forskningsprojekt
Modellering och monitorering av säkerhetshot mot fiberoptiska undervattenskablar
Fiberoptiska nätverk som oavsiktliga mikrofonarrayer: Angreppsvektorer, sårbarheter och försvar
6G Artificial Intelligence Radar
Fysikbaserad djupinlärning för optisk dataöverföring och distribuerad avkänning