The integration of Artificial Intelligence (AI) and emerging 6G networks introduces new opportunities for scalable coordination in tactical autonomous vehicle systems. This paper proposes a communication-centric hierarchical architecture for Tactical Autonomous Defense Vehicle Networks (TADVNs) that models the integration of edge-assisted Large Language Model (LLM) reasoning with 6G-enabled connectivity and semantic communication. The framework is designed to improve coordination efficiency, reduce communication overhead, and enhance latency resilience under increasing fleet-scale operation. Unlike conventional task-specific AI pipelines that rely on structured feature processing and rule-based coordination, the proposed approach incorporates semantic abstraction and context-aware decision support within a layered edge-cloud communication architecture. We evaluate communication and coordination performance via Monte Carlo simulations across fleet sizes of 5-30 vehicles under contested network conditions. Results indicate that at a 30-vehicle scale, the 6G-LLM configuration achieves 75.2% latency reduction (29.1 ms vs. 117.5 ms), a 68.7 percentage point increase in mission success rate (82.9% vs. 14.2%), and an 88.6% reduction in communication overhead compared to a 5G-based conventional AI baseline. These findings demonstrate measurable benefits in coordination and communication when semantic reasoning is combined with low-latency 6G connectivity.

翻译：人工智能（AI）与新兴6G网络的融合为战术自主车辆系统的可扩展协调带来了新机遇。本文提出了一种以通信为中心的层次化架构，用于战术自主防御车网络（TADVNs），该架构将边缘辅助的大语言模型（LLM）推理与6G赋能的连接及语义通信进行集成建模。该框架旨在提升协调效率、降低通信开销，并在不断扩大的车队规模运行中增强延迟韧性。与依赖结构化特征处理及基于规则协调的传统任务特定AI管道不同，所提方法在分层边缘-云通信架构中融入了语义抽象和上下文感知决策支持。我们通过蒙特卡洛模拟，在5-30辆车辆规模及对抗网络条件下评估通信与协调性能。结果表明，在30辆车辆规模下，相较于基于5G的常规AI基线，6G-LLM配置实现了75.2%的延迟降低（29.1毫秒对117.5毫秒），任务成功率提升68.7个百分点（82.9%对14.2%），且通信开销降低88.6%。这些发现证实了将语义推理与低延迟6G连接相结合时，在协调与通信方面可量化的优势。