Agentic-V2X: 小型语言模型用于截止期感知的 5G/6G V2X 调度

Jul 16, 2026

Upload by Violet

Large Language Models (LLMs) are considered as control interfaces for next-generation networks, but their latency, hallucinations, and lack of control guarantees hinder direct use in near-real-time packet scheduling, especially in dynamic V2X environments. The work proposes Agentic-V2X: a locally deployed small language model periodically generates rApp-inspired, structured scheduler policies, while an xApp-like controller executes validated policies at fast intervals. Policies include service priorities, weight bounds, and safety constraints, validated and repaired before ns-3 enforcement.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

Abstract

Introduction

Agentic-V2X architecture and rationale

Problem setting and scheduling trade-offs

Why LLMs are not used directly for scheduling

Agentic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networks Gerasimos Papanikolaou-Ntais∗ , Alexandros Kaloxylos†, and Athanasios Kanavos† ∗ Department of Informatics, University of Piraeus, 185 34 Piraeus, Greece [mpsp2540@unipi.gr](mpsp2540@unipi.gr) †Department of Informatics and Telecommunications, University of Peloponnese, 221 31 Tripoli, Greece {kaloxyl, [kanavosa](kanavosa}@uop.gr)[}](kanavosa}@uop.gr)[@uop.gr](kanavosa}@uop.gr) arXiv :2607 .04290v 1 [ cs .NI ] 5 Jul 2026 Abstract—Large Language Models (LLMs) are increasingly proposed as control interfaces for next-generation networks. However, their latency, occasional hallucinations, and lack of control guarantees make them unsuitable for direct near-realtime packet schedulers. The problem is intensified in the highly dynamic environment of V2X communications. This paper studies a more practical architecture in which a small, locally deployed language model acts as a periodic non-real-time rAppinspired policy creator, while a lightweight xApp-like controller executes validated policies at much faster intervals suitable for scheduling. The proposed framework targets deadline-aware 5G NR V2X scheduling with heterogeneous services, including teleoperated driving, cooperative awareness, HD map sharing, and sensor sharing. Given a scenario summary, service objective, and telemetry metrics, the LLM generates a structured scheduler policy containing service priorities, weight bounds, and safety constraints. A validator checks and repairs the policy before the xApp-like controller enforces it through scheduler-weight adaptation in ns-3/ns3-ai. The evaluation compares proportional fair scheduling, static expert policies, a heuristic xApp, static LLM-generated policies, and LLM-rApp policies with xApplevel enforcement over 126 completed runs. Metrics include deadline-constrained packet reception ratio, tail latency, deadline violations, throughput, fairness, policy validity, and safety interventions. Results show that the adaptive LLM-rApp/xApp design generates valid and executable policies throughout the campaign and remains competitive in several operating points, including improved mean critical reliability over PF at the highest density and favourable medium-density latency/throughput trade-offs. However, paired statistical analysis shows that the adaptive method is not the best aggregate critical-reliability method and remains below the strongest static policies overall. These results support Agentic-V2X as a safe and executable small-LLMassisted policy-generation architecture rather than a universally dominant scheduler. Index Terms—5G NR, V2X, O-RAN, rApp, xApp, small language models, ns-3, ns3-ai, scheduling, QoS, deadline-aware communication, network configuration. I. INTRODUCTION Fifth-generation New Radio (5G NR) and emerging sixthgeneration (6G) networks are evolving toward increasingly autonomous and AI-native architectures. The Open RAN (ORAN) architecture has made this evolution explicit by separating control logic across two distinct timescales: non-realtime applications (rApps) hosted on the Non-RT RIC that reason over policies and objectives over seconds to minutes, and near-real-time applications (xApps) hosted on the NearRT RIC that act on the radio over tens to hundreds of milliseconds [1] . This separation is attractive because it places slow, deliberative reasoning and fast, deterministic actuation indistinct functional roles. Vehicle-to-everything (V2X) communications is one of the most demanding environments in next generation networks. A single cell must simultaneously serve several use cases, such as teleoperated driving (ToD) with strict low-latency requirements, cooperative awareness messages, HD map distribution, and sensor sharing, each with highly different reliability, latency, and throughput needs [2], [3] . Configuring a scheduler to satisfy the strict latency needs of critical servic

FAQs

为什么不能直接把大语言模型用作近实时分组调度器？

LLM 推理时延高且波动，输出也不具备确定性，无法提供有效性、安全性或可界定行为的保证，因此难以胜任 RAN 中最安全关键的近实时控制环节。

Agentic-V2X 的关键分工是什么？

LLM 位于非实时策略层，按较慢频率周期性生成 rApp 风格的结构化调度策略；xApp-like 控制器在固定更快的时间间隔内执行该策略，通过在预定义边界内自适应调度权重。

调度策略是如何生成、验证并执行的？

LLM 基于场景摘要、业务目标与遥测指标生成包含服务优先级、权重范围与安全约束的结构化策略。随后验证器对策略进行检查与修复，校验通过后由 xApp-like 控制器在 ns-3/ns3-ai 中执行相应的调度权重适配。

Jul 16, 2026

Upload by Violet