A Communication-Centric 6G-LLM Architecture for Scalable Tactical Autonomous Defense Vehicle Networks

2026-05-31

AI Summary

This study addresses the challenges of high communication overhead, low coordination efficiency, and stringent latency requirements in large-scale tactical autonomous defense vehicle networks. To overcome these issues, the authors propose a communication-centric hierarchical collaboration architecture that integrates 6G semantic communications, edge-assisted large language model (LLM) inference, and edge-cloud协同 mechanisms. This approach moves beyond conventional task-specific AI systems that rely on structured features and rule-based coordination, enabling context-aware, efficient decision-making and collaboration. Experimental results demonstrate that, in a 30-vehicle scenario, the proposed system reduces end-to-end latency by 75.2% (from 117.5 ms to 29.1 ms), improves task success rate by 68.7 percentage points (from 14.2% to 82.9%), and decreases communication overhead by 88.6% compared to a 5G baseline.

Abstract

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.

Problem

• Tactical Autonomous Defense Vehicle Networks
• 6G
• Large Language Models
• Semantic Communication
• Scalable Coordination

Innovation

• 6G-LLM integration
• semantic communication
• tactical autonomous vehicle networks
• edge-assisted reasoning
• communication-centric architecture