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.
Achievable Rates for Probabilistic Shaping
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abstract
For a layered probabilistic shaping (PS) scheme with a general decoding metric, an achievable rate is derived using Gallager's error exponent approach and the concept of achievable code rates is introduced. Several instances for specific decoding metrics are discussed, including bit-metric decoding, interleaved coded modulation, and hard-decision decoding. It is shown that important previously known achievable rates can also be achieved by layered PS. A practical instance of layered PS is the recently proposed probabilistic amplitude shaping (PAS).
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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.