The training of neural encoders via deep learning necessitates a differentiable channel model due to the backpropagation algorithm. This requirement can be sidestepped by approximating either the channel distribution or its gradient through pilot signals in real-world scenarios. The initial approach draws upon the latest advancements in image generation, utilizing generative adversarial networks (GANs) or their enhanced variants to generate channel distributions. In this paper, we address this channel approximation challenge with diffusion models, which have demonstrated high sample quality in image generation. We offer an end-to-end channel coding framework underpinned by diffusion models and propose an efficient training algorithm. Our simulations with various channel models establish that our diffusion models learn the channel distribution accurately, thereby achieving near-optimal end-to-end symbol error rates (SERs). We also note a significant advantage of diffusion models: A robust generalization capability in high signal-to-noise ratio regions, in contrast to GAN variants that suffer from error floor. Furthermore, we examine the trade-off between sample quality and sampling speed, when an accelerated sampling algorithm is deployed, and investigate the effect of the noise scheduling on this trade-off. With an apt choice of noise scheduling, sampling time can be significantly reduced with a minor increase in SER.

翻译：通过深度学习训练神经编码器需要可微分的信道模型以执行反向传播算法。在实际场景中，这一要求可通过利用导频信号近似信道分布或其梯度来规避。最初的方法借鉴了图像生成领域的最新进展，利用生成对抗网络（GAN）或其增强变体生成信道分布。本文针对该信道近似问题，采用在图像生成中展现出高样本质量的扩散模型进行求解。我们提出一个基于扩散模型的端到端信道编码框架，并设计了一种高效的训练算法。通过多种信道模型的仿真验证，我们的扩散模型能够准确学习信道分布，从而实现接近最优的端到端符号误码率（SER）。我们还注意到扩散模型的显著优势：与遭受错误平层的GAN变体相比，扩散模型在高信噪比区域具备强大的泛化能力。此外，我们探讨了采用加速采样算法时样本质量与采样速度之间的权衡关系，并研究了噪声调度对该权衡的影响。通过合理选择噪声调度策略，可在SER小幅增加的前提下显著缩短采样时间。