With the incredible results achieved from generative pre-trained transformers (GPT) and diffusion models, generative AI (GenAI) is envisioned to yield remarkable breakthroughs in various industrial and academic domains. In this paper, we utilize denoising diffusion probabilistic models (DDPM), as one of the state-of-the-art generative models, for probabilistic constellation shaping in wireless communications. While the geometry of constellations is predetermined by the networking standards, probabilistic constellation shaping can help enhance the information rate and communication performance by designing the probability of occurrence (generation) of constellation symbols. Unlike conventional methods that deal with an optimization problem over the discrete distribution of constellations, we take a radically different approach. Exploiting the ``denoise-and-generate'' characteristic of DDPMs, the key idea is to learn how to generate constellation symbols out of noise, ``mimicking'' the way the receiver performs symbol reconstruction. By doing so, we make the constellation symbols sent by the transmitter, and what is inferred (reconstructed) at the receiver become as similar as possible. Our simulations show that the proposed 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 noise. Notably, a threefold improvement in terms of mutual information is achieved compared to DNN-based approach for 64-QAM geometry.

翻译：随着生成式预训练Transformer（GPT）和扩散模型取得的惊人成果，生成式人工智能（GenAI）有望在工业与学术领域带来显著突破。本文利用去噪扩散概率模型（DDPM）这一最先进的生成模型，实现无线通信中的概率星座整形。尽管星座的几何结构由网络标准预先确定，但概率星座整形可通过设计星座符号的发生（生成）概率来提升信息速率与通信性能。不同于传统方法处理离散星座分布上的优化问题，我们采取了一种截然不同的方式。借助DDPM的“去噪并生成”特性，核心思想在于学习如何从噪声中生成星座符号，从而“模仿”接收端符号重建的过程。通过这种方式，我们使发射端发送的星座符号与接收端推断（重建）的结果尽可能相似。仿真表明，所提方案优于基于深度神经网络（DNN）的基准方法及均匀整形方案，同时在低信噪比（SNR）与非高斯噪声条件下展现了网络鲁棒性与优异的分布外性能。值得注意的是，对于64-QAM星座几何，相较基于DNN的方法，其互信息实现了三倍的提升。