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.
Probabilistic Shaping for the AWGN Channel
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abstract
In this report, we study communication over an additive white Gaussian noise channel with a fixed signal constellation. We measure how much information we can send through this channel and how to improve the rate of communication by changing the input probability distribution. More precisely, we study the mutual information obtained from the Maxwell-Boltzmann distribution, the Blahut-Arimoto algorithm and a constrained version of the Blahut-Arimoto algorithm. We emphasise the fact that the Maxwell--Boltzmann distribution is not optimal. We also observe that the Blahut-Arimoto algorithm does not give us the best mutual information over SNR. To get the optimal distribution for a fixed SNR, we have to implement a constrained version of the Blahut-Arimoto algorithm.
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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.