# Integrating Generative AI with Network Digital Twins for Enhanced Network Operations

arXiv: https://arxiv.org/abs/2406.17112 · HTML: https://mlsanity.com/paper/2406.17112/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Network Digital Twin: Context, Enabling Technologies and Opportunities](https://mlsanity.com/paper/2205.14206/) (2022)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [When Digital Twin Meets Generative AI: Intelligent Closed-Loop Network Management](https://mlsanity.com/paper/2404.03025/) (2024)
- [The Interplay of AI and Digital Twin: Bridging the Gap between Data-Driven and Model-Driven Approaches](https://mlsanity.com/paper/2209.12423/) (2022)
- [Digital Twin Based User-Centric Resource Management for Multicast Short Video Streaming](https://mlsanity.com/paper/2308.08995/) (2023)
- [Digital Twin-Driven Network Architecture for Video Streaming](https://mlsanity.com/paper/2310.19079/) (2023)

## Built on by (most cited first, 2 of 2)

- [The Role and Applications of Airport Digital Twin in Cyberattack Protection during the Generative AI Era](https://mlsanity.com/paper/2408.05248/) (2024)
- [NDT Factory: Synthesizing Verified Network Digital Twins from Semantic Models via Multi-Agent LLM](https://mlsanity.com/paper/2609.12170/) (2026)

## Abstract (arXiv)

As telecommunications networks become increasingly complex, the integration of advanced technologies such as network digital twins and generative artificial intelligence (AI) emerges as a pivotal solution to enhance network operations and resilience. This paper explores the synergy between network digital twins, which provide a dynamic virtual representation of physical networks, and generative AI, particularly focusing on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We propose a novel architectural framework that incorporates these technologies to significantly improve predictive maintenance, network scenario simulation, and real-time data-driven decision-making. Through extensive simulations, we demonstrate how generative AI can enhance the accuracy and operational efficiency of network digital twins, effectively handling real-world complexities such as unpredictable traffic loads and network failures. The findings suggest that this integration not only boosts the capability of digital twins in scenario forecasting and anomaly detection but also facilitates a more adaptive and intelligent network management system.