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:众所周知的问题是,由于学习过程和基于梯度-荧光优化方法,深学习的终端到终端(E2E)通道编码系统取决于已知和不同的频道模式,这给在现实情景中通过实验信号生成的样本来接近或生成频道或其衍生物带来了挑战。目前,有两种普遍的方法可以解决这个问题。一种是通过基因对抗性对立网络(GAN)生成频道,另一种是基本上通过强化学习方法来接近梯度。其他方法包括基于分数的方法、变式自动编码器或基于相互信息的方法。在本文中,我们侧重于基因化模型,特别是称为推广模型的有希望的新方法,这种方法在基于图像的任务中显示了更高质量的生成。我们将表明,扩散模型可以用于无线E2E情景中,并且可以像Wasserstein GANs那样良好,同时具有更稳定的培训程序和测试的更普及能力。