  Diffusion models are at the vanguard of generative AI research with renowned
solutions such as ImageGen by Google Brain and DALL.E 3 by OpenAI.
Nevertheless, the potential merits of diffusion models for communication
engineering applications are not fully understood yet. In this paper, we aim to
unleash the power of generative AI for PHY design of constellation symbols in
communication systems. Although the geometry of constellations is predetermined
according to networking standards, e.g., quadrature amplitude modulation (QAM),
probabilistic shaping can design the probability of occurrence (generation) of
constellation symbols. This can help improve the information rate and decoding
performance of communication systems. We exploit the ``denoise-and-generate''
characteristics of denoising diffusion probabilistic models (DDPM) for
probabilistic constellation shaping. The key idea is to learn generating
constellation symbols out of noise, ``mimicking'' the way the receiver performs
symbol reconstruction. This way, we make the constellation symbols sent by the
transmitter, and what is inferred (reconstructed) at the receiver become as
similar as possible, resulting in as few mismatches as possible. Our results
show that the generative AI-based scheme outperforms deep neural network
(DNN)-based benchmark and uniform shaping, while providing network resilience
as well as robust out-of-distribution performance under low-SNR regimes and
non-Gaussian assumptions. Numerical evaluations highlight 30% improvement in
terms of cosine similarity and a threefold improvement in terms of mutual
information compared to DNN-based approach for 64-QAM geometry.
