NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation

TL;DR

NetDiff is a novel deep graph diffusion model that generates realistic wireless ad hoc network topologies, improving global control and physical constraint compliance, facilitating network evolution and reducing computational burden.

Contribution

It introduces a graph denoising diffusion architecture with enhanced global control and physical constraint integration for wireless network topology generation.

Findings

Generated topologies are realistic and structurally similar to dataset graphs.

Requires only minor corrections for operational deployment.

Supports stable network evolution over time.

Abstract

This work introduces NetDiff, an expressive graph denoising diffusion probabilistic architecture that generates wireless ad hoc network link topologies. Such networks, with directional antennas, can achieve unmatched performance when the communication links are designed to provide good geometric properties, notably by reducing interference between these links while respecting diverse physical constraints. How to craft such a link assignment algorithm is yet a real problem. Deep graph generation offers multiple advantages compared to traditional approaches: it allows to relieve the network nodes of the communication burden caused by the search of viable links and to avoid resorting to heavy combinatorial methods to find a good link topology. Denoising diffusion also provides a built-in method to update the network over time. Given that graph neural networks sometimes tend to struggle with global, structural properties, we augment the popular graph transformer with cross-attentive modulation tokens in order to improve global control over the predicted topology. We also incorporate simple node and edge features, as well as additional loss terms, to facilitate the compliance with the network topology physical constraints. A network evolution algorithm based on partial diffusion is also proposed to maintain a stable network topology over time when the nodes move. Our results show that the generated links are realistic, present structural properties similar to the dataset graphs', and require only minor corrections and verification steps to be operational.