Network optimization is a fundamental challenge in the Internet of Things (IoT) network, often characterized by complex features that make it difficult to solve these problems. Recently, generative diffusion models (GDMs) have emerged as a promising new approach to network optimization, with the potential to directly address these optimization problems. However, the application of GDMs in this field is still in its early stages, and there is a noticeable lack of theoretical research and empirical findings. In this study, we first explore the intrinsic characteristics of generative models. Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization. Based on this exploration, we implement GDMs as optimizers aimed at learning high-quality solution distributions for given inputs, sampling from these distributions during inference to approximate or achieve optimal solutions. Specifically, we utilize denoising diffusion probabilistic models (DDPMs) and employ a classifier-free guidance mechanism to manage conditional guidance based on input parameters. We conduct extensive experiments across three challenging network optimization problems. By investigating various model configurations and the principles of GDMs as optimizers, we demonstrate the ability to overcome prediction errors and validate the convergence of generated solutions to optimal solutions.We provide code and data at https://github.com/qiyu3816/DiffSG.

翻译：网络优化是物联网（IoT）网络中的一项基础性挑战，通常具有复杂的特征，使得这些问题难以求解。近年来，生成式扩散模型（GDMs）作为一种有前景的网络优化新方法出现，具备直接解决这些优化问题的潜力。然而，GDMs在该领域的应用仍处于早期阶段，且明显缺乏理论研究和实证发现。在本研究中，我们首先探讨了生成模型的内在特性。接着，我们提供了生成模型在网络优化中优于判别模型的简明理论证明和直观展示。基于此探索，我们将GDMs实现为优化器，旨在学习给定输入的高质量解分布，并在推理过程中从这些分布中采样以逼近或获得最优解。具体而言，我们利用去噪扩散概率模型（DDPMs），并采用无分类器引导机制来管理基于输入参数的条件引导。我们在三个具有挑战性的网络优化问题上进行了广泛的实验。通过研究不同的模型配置以及GDMs作为优化器的原理，我们证明了其克服预测误差的能力，并验证了生成解向最优解的收敛性。代码和数据可在 https://github.com/qiyu3816/DiffSG 获取。