Diffusion models are at the vanguard of generative AI research with renowned solutions such as ImageGen by Google Brain and DALL.E 3 by OpenAI. Nevertheless, the potential merits of diffusion models for communication engineering applications are not fully understood yet. In this paper, we aim to unleash the power of generative AI for PHY design of constellation symbols in communication systems. Although the geometry of constellations is predetermined according to networking standards, e.g., quadrature amplitude modulation (QAM), probabilistic shaping can design the probability of occurrence (generation) of constellation symbols. This can help improve the information rate and decoding performance of communication systems. We exploit the ``denoise-and-generate'' characteristics of denoising diffusion probabilistic models (DDPM) for probabilistic constellation shaping. The key idea is to learn generating constellation symbols out of noise, ``mimicking'' the way the receiver performs symbol reconstruction. This way, we make the constellation symbols sent by the transmitter, and what is inferred (reconstructed) at the receiver become as similar as possible, resulting in as few mismatches as possible. Our results show that the generative AI-based scheme outperforms deep neural network (DNN)-based benchmark and uniform shaping, while providing network resilience as well as robust out-of-distribution performance under low-SNR regimes and non-Gaussian assumptions. Numerical evaluations highlight 30% improvement in terms of cosine similarity and a threefold improvement in terms of mutual information compared to DNN-based approach for 64-QAM geometry.

翻译：扩散模型处于生成式AI研究的前沿，诸如谷歌大脑的ImageGen和OpenAI的DALL·E 3等知名解决方案均基于此。然而，扩散模型在通信工程应用中的潜在优势尚未得到充分理解。本文旨在释放生成式AI在通信系统星座符号物理层设计中的潜力。尽管星座的几何形状根据网络标准（如正交幅度调制）预先确定，但概率成形可设计星座符号出现的概率。这有助于提升通信系统的信息速率和译码性能。我们利用去噪扩散概率模型的“去噪-生成”特性实现概率星座成形。其核心思想是从噪声中学习生成星座符号，“模仿”接收端执行符号重构的方式。由此，发射端发送的星座符号与接收端推断的符号尽可能相似，从而最大限度减少失配。结果表明，基于生成式AI的方案优于深度神经网络基准方案和均匀成形方案，同时在低信噪比和非高斯假设下具备网络弹性与稳健的分布外性能。数值评估显示，在64-QAM几何结构下，该方法相较于DNN方法在余弦相似度上提升30%，互信息量提升三倍。