deep generative models are key-enabling technology to computer vision, text
generation and large language models. denoising diffusion probabilistic models
(DDPMs) have recently gained much attention due to their
本文提出了基于去噪扩散概率模型的无线通信方案,旨在解决实际应用中的硬件损伤、信道失真和量化误差等非理想因素,提供低信噪比下的网络韧性、对不同硬件损伤水平和量化误差的近不变重建性能,以及抵抗非高斯噪声的强大分布外表现,并通过余弦相似度和均方误差(MSE)评估与传统深度神经网络(DNN)接收机相比的超过 25 dB 改进的重建性能。