Autoregressive learning approach improves achievable information rate by 0.3 bits/2D for dual-polarized 64-QAM transmission, indicating advanced performance on nonlinear fiber channels.
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 provides a 0.3-bits/2D achievable information rate gain over an optimized marginal distribution for dual-polarized 64-QAM transmission over a single-span 205 km link.
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Askari et al. (2025) studied this question.
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