AI-Generated Network Design: A Diffusion Model-based Learning Approach

TL;DR

This paper introduces AIGN, a diffusion model-based AI approach for automated, customizable network design that reduces manual effort and enhances adaptability in complex, dynamic network environments.

Contribution

The paper presents a novel intention-driven paradigm using diffusion models to generate diverse, optimized network solutions without human expertise.

Findings

AIGN effectively guides transmit power allocation in digital twin access networks.

The approach can adapt to various objectives and constraints.

AIGN demonstrates potential for creating innovative network designs.

Abstract

The future networks pose intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirements, and complicated manual configuration. However, traditional rule-based solutions heavily rely on human efforts and expertise, while data-driven intelligent algorithms still lack interpretability and generalization. In this paper, we propose the AIGN (AI-Generated Network), a novel intention-driven paradigm for network design, which allows operators to quickly generate a variety of customized network solutions and achieve expert-free problem optimization. Driven by the diffusion model-based learning approach, AIGN has great potential to learn the reward-maximizing trajectories, automatically satisfy multiple constraints, adapt to different objectives and scenarios, or even intelligently create novel designs and mechanisms unseen in existing network environments. Finally, we conduct a use case to demonstrate that AIGN can effectively guide the design of transmit power allocation in digital twin-based access networks.