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LLM-Guided Dual-Stage Latency Optimization for Closed-Loop 6G V2I Digital Twins — arXiv:2610.22848

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.22848
License (as archived): CC0-1.0
Archived: 2026-10-19T09:37:28.020Z
SHA-256 of the archived text: 53f2280c4f19ea0e0c3fea6ebef24c2f638e78cc9be3544e859350f6621070c5
Stable URL of this page: https://metraipolis.com/documento/dc_b4a00a8a2d8f

How to cite

LLM-Guided Dual-Stage Latency Optimization for Closed-Loop 6G V2I Digital Twins — arXiv:2610.22848 — arXiv (en), https://arxiv.org/abs/2610.22848 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b4a00a8a2d8f (Metraipolis archive dc_b4a00a8a2d8f, sha256 53f2280c4f19ea0e0c3fea6ebef24c2f638e78cc9be3544e859350f6621070c5)

BibTeX

@misc{dcb4a00a8a2d8f,
  title = {LLM-Guided Dual-Stage Latency Optimization for Closed-Loop 6G V2I Digital Twins — arXiv:2610.22848},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.22848},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b4a00a8a2d8f; sha256 53f2280c4f19ea0e0c3fea6ebef24c2f638e78cc9be3544e859350f6621070c5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.22848. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Wireless Digital Twins are emerging as a key enabler for predictive network control in 6G Vehicle-to-Infrastructure (V2I) systems. However, closed-loop V2I DTs face two dual-stage communication bottlenecks arising from model parameter exchange for V2I DT reconstruction and multi-service V2I traffic delivery after each reconstruction. This paper proposes a closed-loop dual-stage End-to-End (E2E) latency optimization framework that integrates Compressed Personalized Federated Learning with a unified Large-Language-Model-guided Multi-Agent Reinforcement Learning approach. A CPFL-enabled decision-oriented V2I DT is first constructed by jointly predicting Path Loss (PL) and Quality-of-Experience (QoE)-oriented Key Performance Indicators (KPIs). Then the framework comprises two temporally coupled but latency-wise distinct optimization stages: Stage I minimizes the joint Uplink (UL) and Downlink (DL) E2E transmission latency of compressed shared backbone's parameters, and Stage II minimizes the service-aware E2E latency of multi-service V2I traffic. As the two stages share aligned agent roles and action space, we develop a unified Semantic-Aware Guidance-Enhanced Multi-Agent Proximal Policy Optimization framework, in which the LLM acts as a training-time semantic planner that provides high-level traffic splitting and power allocation guidance to reduce inefficient exploration, while the MARL agents execute feasible low-level actions. Simulation results demonstrate that the proposed framework enables efficient V2I DT reconstruction and significantly reduces both E2E latency for DT reconstruction and multi-service V2I traffic. The proposed SAGE-MAPPO supports cross-stage policy reuse by sequentially solving the two stages within each DT update cycle, which accelerates convergence relative to the existing baselines and facilitates timely DT updates in dynamic V2I scenarios.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.NI, nuovi lavori del giorno).
Categorie arXiv: Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2610.22848 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:32:29.695Z dalla pagina https://arxiv.org/list/cs.NI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).