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Empowering Wireless Networks with Artificial Intelligence Generated Graph

Jiacheng Wang, Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang, Haibo Zhou, Dong In Kim

TL;DR

The study addresses the growing need to optimize wireless networks beyond traditional graph neural networks by leveraging Generative AI to produce graphs conditioned on user-defined problems. It analyzes major GAI models for graph generation, proposes a diffusion-based conditional graph-generation framework comprising a denoising network and an evaluation network trained via reward signals, and demonstrates its applicability through an ISAC link-selection case study. The findings show that the framework can generate problem-specific graphs that enable resource-efficient sensing and communication activation, with convergence and performance gains over baselines. This work advances adaptive, condition-driven graph synthesis for wireless-network optimization, offering a practical path toward more flexible and robust network design under complex constraints.

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.

Empowering Wireless Networks with Artificial Intelligence Generated Graph

TL;DR

The study addresses the growing need to optimize wireless networks beyond traditional graph neural networks by leveraging Generative AI to produce graphs conditioned on user-defined problems. It analyzes major GAI models for graph generation, proposes a diffusion-based conditional graph-generation framework comprising a denoising network and an evaluation network trained via reward signals, and demonstrates its applicability through an ISAC link-selection case study. The findings show that the framework can generate problem-specific graphs that enable resource-efficient sensing and communication activation, with convergence and performance gains over baselines. This work advances adaptive, condition-driven graph synthesis for wireless-network optimization, offering a practical path toward more flexible and robust network design under complex constraints.

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.
Paper Structure (27 sections, 5 figures)

This paper contains 27 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Applications of Graph Generation
  3. Applications in Molecular Design
  4. Drug Molecule Generation
  5. Protein Structure Design
  6. Applications in Wireless Communication Networks
  7. Routing Optimization
  8. Resource Allocation
  9. Lessons Learned
  10. Generative AI Models for Graph Generation
  11. Auto-Regressive Model
  12. Variational Auto-encoders
  13. Normalizing Flows
  14. Generative Adversarial Networks
  15. Denoising Diffusion Model
  16. ...and 12 more sections

Figures (5)

  • Figure 1: The applications of graph in molecule generation, D2D communications, cellular networks, and cell-free communication systems. In JT-VAE, An input molecule undergoes encoding via a tree and graph encoder, which generates corresponding tree and graph latent vectors. These vectors are then utilized by a tree decoder and a graph assembler for the purpose of reconstructing the molecule. Additionally, the tree and graph latent vectors are combined and used as input for a model that predicts the pIC50 value of the input molecule. In D2D communication and cellular networks, graphs offer a structured way to represent the transmission links between devices and base stations (BSs), enhancing the management of link interference. In cell-free communication systems, graphs simplify the connections between user equipment (UEs) and access points (APs). This simplification aids in optimizing cooperative transmission and reception, improving power control, and facilitating efficient resource sharing across the network.
  • Figure 2: The summary of GAI models from the perspective of graph generation.
  • Figure 3: The structure of the proposed framework. Our framework comprises two core modules, i.e., the denoising network and evaluation module. During training, the first step is to input the noisy image, the denoising time step, and conditions, based on which the denoising network generates the graphs. In Step 2, the defined reward function is used to evaluate the generated graphs. Finally, in Step 3, the cumulative gradient is calculated based on the reward and the result is fed back to the denoising network for optimizing its parameters. Once trained, the denoising network is capable of generating graphs based on newly input conditions.
  • Figure 4: The training curve of the proposed method and the comparison with other methods.
  • Figure 5: The denoising process of the trained network. Green dots represent nodes, purple cross indicates the target location, black lines represent activated links, and red dashed lines mark links that contribute little to sensing. The shaded area is the Fresnel zone formed by the activated link. Here, a total of 50 denoising steps are executed to obtain the final graph, with the results at steps 0, 10, 20, 30, 40, and 50 being displayed.