  It is a known problem that deep-learning-based end-to-end (E2E) channel
coding systems depend on a known and differentiable channel model, due to the
learning process and based on the gradient-descent optimization methods. This
places the challenge to approximate or generate the channel or its derivative
from samples generated by pilot signaling in real-world scenarios. Currently,
there are two prevalent methods to solve this problem. One is to generate the
channel via a generative adversarial network (GAN), and the other is to, in
essence, approximate the gradient via reinforcement learning methods. Other
methods include using score-based methods, variational autoencoders, or
mutual-information-based methods. In this paper, we focus on generative models
and, in particular, on a new promising method called diffusion models, which
have shown a higher quality of generation in image-based tasks. We will show
that diffusion models can be used in wireless E2E scenarios and that they work
as good as Wasserstein GANs while having a more stable training procedure and a
better generalization ability in testing.
