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.

翻译：深度学习驱动的端到端信道编码系统依赖于已知且可微的信道模型，这是由于其基于梯度下降优化方法的学习过程所导致的已知问题。这使得在实际场景中，需要通过导频信号生成的样本来近似或生成信道及其导数。目前解决该问题的主流方法有两种：一是通过生成对抗网络生成信道，二是本质上通过强化学习方法近似梯度。其他方法还包括基于评分的方法、变分自编码器或互信息方法。本文聚焦于生成模型，特别是扩散模型这一新兴方法——该类模型在图像任务中已展现出更优的生成质量。我们将证明，扩散模型可应用于无线端到端场景，在保持更稳定的训练过程和更强的测试泛化能力的同时，其性能与Wasserstein生成对抗网络相当。