  With the incredible results achieved from generative pre-trained transformers
(GPT) and diffusion models, generative AI (GenAI) is envisioned to yield
remarkable breakthroughs in various industrial and academic domains. In this
paper, we utilize denoising diffusion probabilistic models (DDPM), as one of
the state-of-the-art generative models, for probabilistic constellation shaping
in wireless communications. While the geometry of constellations is
predetermined by the networking standards, probabilistic constellation shaping
can help enhance the information rate and communication performance by
designing the probability of occurrence (generation) of constellation symbols.
Unlike conventional methods that deal with an optimization problem over the
discrete distribution of constellations, we take a radically different
approach. Exploiting the ``denoise-and-generate'' characteristic of DDPMs, the
key idea is to learn how to generate constellation symbols out of noise,
``mimicking'' the way the receiver performs symbol reconstruction. By doing so,
we make the constellation symbols sent by the transmitter, and what is inferred
(reconstructed) at the receiver become as similar as possible. Our simulations
show that the proposed 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
noise. Notably, a threefold improvement in terms of mutual information is
achieved compared to DNN-based approach for 64-QAM geometry.
