72 citations · 410 across the 39 of their papers we have counts for
6 papers · 1 filter
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
Kadir Gümüs, Alex Alvarado, Bin Chen +2
GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates…
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
Fabrizio Carpi, Christian Häger, Marco Martalò +2
In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve…
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
Christian Häger, Henry D. Pfister, Rick M. Bütler +2
For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show…
Learning Physical-Layer Communication with Quantized Feedback
Jinxiang Song, Bile Peng, Christian Häger +2
Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work…
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
Mengke Lian, Fabrizio Carpi, Christian Häger +1
We consider the weighted belief-propagation (WBP) decoder recently proposed by Nachmani et al. where different weights are introduced for each Tanner graph edge and optimized using…
What Can Machine Learning Teach Us about Communications?
Mengke Lian, Christian Häger, Henry D. Pfister
Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-…