Learning End-to-End Channel Coding with Diffusion Models

• URL: http://arxiv.org/abs/2302.01714v2
• Date: Wed, 29 Nov 2023 14:54:04 GMT
• Title: Learning End-to-End Channel Coding with Diffusion Models
• Authors: Muah Kim, Rick Fritschek, Rafael F. Schaefer
• Abstract summary: 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.
• Score: 22.258823033281356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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