Innovative foundation models, such as GPT-4 and stable diffusion models, have
made a paradigm shift in the realm of artificial intelligence (AI) towards
generative AI-based systems. AI and machine learning (AI/ML) algorithms are
envisioned to be pervasively incorporated into the future wireless
communications systems. In this article, we outline the applications of
diffusion models in wireless communication systems, which are a new family of
probabilistic generative models that have showcased state-of-the-art
performance. The key idea is to decompose data generation process over
"denoising" steps, gradually generating samples out of noise. Based on two case
studies presented, we show how diffusion models can be employed for the
development of resilient AI-native communication systems. Specifically, we
propose denoising diffusion probabilistic models (DDPM) for a wireless
communication scheme with non-ideal transceivers, where 30% improvement is
achieved in terms of bit error rate. In the other example, DDPM is employed at
the transmitter to shape the constellation symbols, highlighting a robust
out-of-distribution performance