Importance-sampling weighted loss functions enable autoencoder-based probabilistic constellation shaping with exact automatic-differentiation gradients, matching prior methods in AWGN and IM/DD simulations.
End-to-end learning of joint geometric and probabilistic constellation shaping,
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End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling
Importance-sampling weighted loss functions enable autoencoder-based probabilistic constellation shaping with exact automatic-differentiation gradients, matching prior methods in AWGN and IM/DD simulations.