72 citations · 410 across the 39 of their papers we have counts for
8 papers · 1 filter
Pruning and Quantizing Neural Belief Propagation Decoders
Andreas Buchberger, Christian Häger, Henry D. Pfister +2
We consider near maximum-likelihood (ML) decoding of short linear block codes. In particular, we propose a novel decoding approach based on neural belief propagation (NBP) decoding…
Learned Decimation for Neural Belief Propagation Decoders
Andreas Buchberger, Christian Häger, Henry D. Pfister +2
We introduce a two-stage decimation process to improve the performance of neural belief propagation (NBP), recently introduced by Nachmani et al., for short low-density parity-chec…
Physics-Based Deep Learning for Fiber-Optic Communication Systems
Christian Häger, Henry D. Pfister
We propose a new machine-learning approach for fiber-optic communication systems whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our main observa…
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Rick M. Bütler, Christian Häger, Henry D. Pfister +2
In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The re…
Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
Vinícius Oliari, Sebastiaan Goossens, Christian Häger +7
Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch''. One guiding principle for previous work on…
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Jinxiang Song, Christian Häger, Jochen Schröder +2
End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO t…
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
Christian Häger, Henry D. Pfister, Rick M. Bütler +2
We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation. This approach performs…
Pruning Neural Belief Propagation Decoders
Andreas Buchberger, Christian Häger, Henry D. Pfister +2
We consider near maximum-likelihood (ML) decoding of short linear block codes based on neural belief propagation (BP) decoding recently introduced by Nachmani et al.. While this me…