Christian Häger
Christian Häger is an assistant professor in the Communication Systems research group. He received the Dipl.-Ing. degree (M.Sc. equivalent) in electrical engineering from Ulm University, Germany, in 2011 and his Ph.D. degree in communication theory from Chalmers University of Technology, Sweden, in 2016. From 2016 until 2019, he was a postdoctoral researcher at the Department of Electrical and Computer Engineering at Duke University, USA. Since 2017, he is a postdoctoral researcher at the Department of Electrical Engineering at Chalmers University of Technology. His research interests include modern coding theory, fiber-optic communications, and machine learning. He received the Marie Sklodowska-Curie Global Fellowship from the European Commission in 2017.
Personal web page: www.christianhaeger.de
Showing 76 publications
Learning Gradient-Based Feed-Forward Equalizer for VCSELs
Unsupervised Learning for Gain-Phase Impairment Calibration in ISAC Systems
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation
Deep-Learning-Based Channel Estimation for Distributed MIMO with 1-bit Radio-Over-Fiber Fronthaul
Real-Time Implementation of Machine-Learning DSP
Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
Spatial Signal Design for Positioning via End-to-End Learning
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Physics-Informed Neural Networks for Studying Charge Dynamics in Air
Model-Driven End-to-End Learning for Integrated Sensing and Communication
Improved Polarization Tracking in the Presence of PDL
FPGA-based Optical Kerr Effect Emulator
Experimental Demonstration of Learned Pulse Shaping Filter for Superchannels
Machine learning for long-haul optical systems
Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
End-to-End Learning for Integrated Sensing and Communication
Data-Driven Estimation of Capacity Upper Bounds
Learning Optimal PAM Levels for VCSEL-based Optical Interconnects
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
Pruning and Quantizing Neural Belief Propagation Decoders
Autoencoder-Based Unequal Error Protection Codes
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Physics-Based Deep Learning for Fiber-Optic Communication Systems
Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
Over-the-fiber Digital Predistortion Using Reinforcement Learning
Learned Decimation for Neural Belief Propagation Decoders
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairments
Decoding Reed-Muller Codes Using Redundant Code Constraints
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Pruning Neural Belief Propagation Decoders
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
Learning Physical-Layer Communication with Quantized Feedback
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep learning
What Can Machine Learning Teach Us about Communications
On Low-Complexity Decoding of Product Codes for High-Throughput Fiber-Optic Systems
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Nonlinear Interference Mitigation via Deep Neural Networks
Decoding Reed-Muller Codes Using Minimum- Weight Parity Checks
Approaching Miscorrection-Free Performance of Product Codes with Anchor Decoding
Miscorrection-free Decoding of Staircase Codes
Density Evolution and Error Floor Analysis for Staircase and Braided Codes
Analysis and Design of Spatially-Coupled Codes with Application to Fiber-Optical Communications
On the Information Loss of the Max-Log Approximation in BICM Systems
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
On Parameter Optimization for Staircase Codes
Spatially-Coupled Codes for Optical Communications: State-of-the-Art and Open Problems
On Signal Constellations and Coding for Long-Haul Fiber-Optical Systems
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
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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Showing 2 research projects
6G Artificial Intelligence Radar
Physics-Based Deep Learning for Optical Data Transmission and Distributed Sensing