164 citations · 1.2k across the 72 of their papers we have counts for
12 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…
Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing
Sarah Brandsen, Kevin D. Stubbs, Henry D. Pfister
Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, w…
Successive Cancellation Decoding of Single Parity-Check Product Codes: Analysis and Improved Decoding
Mustafa Cemil Coşkun, Gianluigi Liva, Alexandre Graell i Amat +2
A product code with single parity-check component codes can be described via the tools of a multi-kernel polar code, where the rows of the generator matrix are chosen according to…