3 papers
cs.LG2026
Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We introduce neural probabilistic amplitude shaping, a joint-distribution learning framework for coherent fiber systems. The proposed scheme provides a 0.5 dB signal-to-noise ratio…
cs.LG2025
Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We present an autoregressive end-to-end learning approach for probabilistic shaping on nonlinear fiber channels. Our proposed scheme learns the joint symbol distribution and provid…
eess.SP2024
Probabilistic Shaping for Nonlinearity Tolerance
Mohammad Taha Askari, Lutz Lampe
Optimizing the input probability distribution of a discrete-time channel is a standard step in the information-theoretic analysis of digital communication systems. Nevertheless, ma…