Christian Häger
Christian Häger är forskarassistent i forskargruppen Kommunikationssystem. Hans forskning inkluderar digital kommunikation, maskininlärning, och kanalkodnings-teori.
Personlig hemsida: www.christianhaeger.de
Visar 67 publikationer
Real-Time Implementation of Machine-Learning DSP
Spatial Signal Design for Positioning via End-to-End Learning
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
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
Physics-Informed Neural Networks for Studying Charge Dynamics in Air
Model-based end-to-end learning for multi-target integrated sensing and communication
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Improved Polarization Tracking in the Presence of PDL
FPGA-based Optical Kerr Effect Emulator
End-to-End Learning for Integrated Sensing and Communication
Experimental Demonstration of Learned Pulse Shaping Filter for Superchannels
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
Machine learning for long-haul optical systems
Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
Pruning and Quantizing Neural Belief Propagation Decoders
Autoencoder-Based Unequal Error Protection Codes
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
Physics-Based Deep Learning for Fiber-Optic Communication Systems
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairments
Over-the-fiber Digital Predistortion Using Reinforcement Learning
Learned Decimation for Neural Belief Propagation Decoders
Decoding Reed-Muller Codes Using Redundant Code Constraints
Pruning Neural Belief Propagation Decoders
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
Approaching Miscorrection-Free Performance of Product Codes with Anchor Decoding
Nonlinear Interference Mitigation via Deep Neural Networks
Decoding Reed-Muller Codes Using Minimum- Weight Parity Checks
Miscorrection-free Decoding of Staircase 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
Density Evolution and Error Floor Analysis for Staircase and Braided Codes
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
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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6G Artificial Intelligence Radar
Fysikbaserad djupinlärning för optisk dataöverföring och distribuerad avkänning