Empowering Wireless Networks with Artificial Intelligence Generated Graph

TL;DR

This paper introduces a GAI-based graph generation framework to enhance wireless network optimization, leveraging diffusion models and customizable conditions for improved graph analysis and network problem solving.

Contribution

It proposes a novel GAI-driven graph generation framework for wireless networks, integrating conditional diffusion models and evaluation networks for customizable graph creation.

Findings

Framework effectively generates graphs based on user conditions

Validated with link selection in integrated sensing and communication

Demonstrates potential for improved network optimization

Abstract

In wireless communications, transforming network into graphs and processing them using deep learning models, such as Graph Neural Networks (GNNs), is one of the mainstream network optimization approaches. While effective, the generative AI (GAI) shows stronger capabilities in graph analysis, processing, and generation, than conventional methods such as GNN, offering a broader exploration space for graph-based network optimization. Therefore, this article proposes to use GAI-based graph generation to support wireless networks. Specifically, we first explore applications of graphs in wireless networks. Then, we introduce and analyze common GAI models from the perspective of graph generation. On this basis, we propose a framework that incorporates the conditional diffusion model and an evaluation network, which can be trained with reward functions and conditions customized by network designers and users. Once trained, the proposed framework can create graphs based on new conditions, helping to tackle problems specified by the user in wireless networks. Finally, using the link selection in integrated sensing and communication (ISAC) as an example, the effectiveness of the proposed framework is validated.