# LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks

arXiv: https://arxiv.org/abs/2506.18293 · HTML: https://mlsanity.com/paper/2506.18293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2025.

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

- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks](https://mlsanity.com/paper/2608.06227/) (2026)

## Abstract (arXiv)

Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions. Digital twins (DTs) offer a powerful tool for network management by creating high-fidelity virtual replicas that model real-time network behavior, while large language models (LLMs) enhance decision-making through their advanced generalization and contextual reasoning capabilities. This article proposes LLM-driven DTs for network optimization (LLM-DTNet), a hierarchical framework that integrates multi-layer DT architectures with LLM-based orchestration to enable adaptive, real-time RRM in heterogeneous NextG networks. We present the fundamentals and design considerations of LLM-DTNet while discussing its effectiveness in proactive and situation-aware network management across terrestrial and non-terrestrial applications. Furthermore, we highlight key challenges, including scalable DT modeling, secure LLM-DT integration, energy-efficient implementations, and multimodal data processing, shaping future advancements in NextG intelligent wireless networks.