Learning End-to-End Channel Coding with Diffusion Models
This addresses the challenge of approximating or generating channel models from pilot signals in wireless communication, but it is incremental as it builds on existing generative model approaches.
The paper tackles the problem of deep-learning-based end-to-end channel coding systems requiring known and differentiable channel models by proposing the use of diffusion models as generative models. The result shows that diffusion models perform as well as Wasserstein GANs, with more stable training and better generalization in testing.
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.