72 citations · 410 across the 39 of their papers we have counts for
5 papers · 1 filter
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
Christian Häger, Henry D. Pfister
We propose a low-complexity sub-banded DSP architecture for digital backpropagation where the walk-off effect is compensated using simple delay elements. For a simulated 96-Gbaud s…
ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters
Christoffer Fougstedt, Christian Häger, Lars Svensson +2
We consider time-domain digital backpropagation with chromatic dispersion filters jointly optimized and quantized using machine-learning techniques. Compared to the baseline implem…
Decoding Reed-Muller Codes Using Minimum-Weight Parity Checks
Elia Santi, Christian Häger, Henry D. Pfister
Reed-Muller (RM) codes exhibit good performance under maximum-likelihood (ML) decoding due to their highly-symmetric structure. In this paper, we explore the question of whether th…
Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
Christian Häger, Henry D. Pfister
An important problem in fiber-optic communications is to invert the nonlinear Schrödinger equation in real time to reverse the deterministic effects of the channel. Interestingly,…
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Shen Li, Christian Häger, Nil Garcia +1
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping…