Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Sequential Neural Probabilistic Amplitude Shaping: Learning the Channel's Language
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
We present the first neural probabilistic amplitude shaping that outperforms existing methods while accounting for all implementation losses, using a block-less, easily implementab…
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…