Learning End-to-End Channel Coding with Diffusion Models

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review



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.


Original languageEnglish
Title of host publicationThe proceedings of the 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding.
PublisherDie Informationstechnische Gesellschaft (ITG) im Verband der Elektrotechnik Elektronik Informationstechnik e.V ITG
Number of pages6
ISBN (electronic)9783800760510
Publication statusPublished - 2023

External IDs

Scopus 85166968774



  • channel estimation, diffusion models, end-to-end learning, generative networks, diffusion model, end-to-end leaning