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Probabilistic Shaping for the AWGN Channel

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arxiv 2302.08388 v1 pith:IVZUBW7X submitted 2023-02-16 cs.IT math.IT

Sébastien Delsad This is my paper · ORCID
classification cs.ITmath.IT
keywords algorithmblahut-arimotodistributionchannelinformationcommunicationconstrainedfixed
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. End-to-End Learning of Probabilistic Constellation Shaping through Importance Sampling

    cs.IT 2025-06 conditional novelty 6.0 of 10

    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.

  2. Information Theoretic Perspective on Representation Learning

    cs.IT 2026-01 conditional novelty 2.0 of 10

    Representation learning limits are restatements of Shannon's source coding, channel coding, and rate-distortion theorems under a dictionary mapping embedding size to code rate.

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