Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels
arXiv:2602.02716v11 citations
Originality Incremental advance
AI Analysis
This work addresses performance enhancement in fiber-optic communication systems, representing an incremental improvement.
The paper tackled the problem of improving signal-to-noise ratio in coherent fiber systems by introducing neural probabilistic amplitude shaping, achieving a 0.5 dB gain over sequence selection for dual-polarized 64-QAM transmission over a 205 km link.
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.