Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels
Mohammad Taha Askari, Lutz Lampe, Amirhossein Ghazisaeidi
TL;DR
This paper presents a neural probabilistic amplitude shaping method that improves signal quality in fiber optic communication systems by learning joint distributions, achieving significant SNR gains over traditional sequence selection methods.
Contribution
It introduces a novel neural probabilistic amplitude shaping framework for coherent fiber systems, demonstrating improved performance in long-distance optical transmission.
Findings
0.5 dB SNR gain over sequence selection
Effective joint-distribution learning for amplitude shaping
Enhanced performance in 205 km fiber link
Abstract
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 gain over sequence selection for dual-polarized 64-QAM transmission across a single-span 205 km link.
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Taxonomy
TopicsOptical Network Technologies · Neural Networks and Reservoir Computing · Advanced Photonic Communication Systems