Diffusion Models as Network Optimizers: Explorations and Analysis

TL;DR

This paper investigates the use of generative diffusion models as optimizers for complex network problems in IoT, providing theoretical insights and extensive experiments demonstrating their effectiveness and convergence to optimal solutions.

Contribution

It introduces the novel application of diffusion models as network optimizers, with theoretical proof and empirical validation across multiple challenging problems.

Findings

Diffusion models can effectively learn solution distributions for network optimization.

GDMs demonstrate convergence to optimal solutions in experiments.

The approach overcomes prediction errors in complex network scenarios.

Abstract

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.