Agentic AI-Driven UAV Network Deployment: An LLM-Enhanced Exact Potential Game Approach

TL;DR

This paper introduces an AI-enhanced game-theoretic framework for optimizing UAV network deployment, improving connectivity, throughput, and latency in dynamic environments using large language models for adaptive decision-making.

Contribution

It develops a dual-scale optimization approach based on exact potential games, integrating LLMs for automatic utility weight generation to enhance adaptability and performance.

Findings

Outperforms baseline methods in energy efficiency, latency, and throughput.

Effectively optimizes link configurations and deployment parameters.

Enhances adaptability with LLM-driven utility weight generation.

Abstract

Unmanned aerial vehicular network (UAVN) is envisioned to provide flexible connectivity, wide-area coverage, and low-latency services in dynamic environments. From an agentic artificial intelligence (Agentic AI) perspective, UAVNs naturally operate as multi-agent systems, where UAVs act as intelligent agents that coordinate deployment and networking decisions to achieve global performance objectives. However, the strong coupling between discrete link decisions and continuous deployment parameters makes UAVN deployment optimization a mixed-integer nonconvex problem, resulting in challenges in scalability, efficiency, and solution consistency under dynamic network conditions. This paper proposes a dual spatial-scale UAVN deployment optimization framework based on exact potential games (EPGs), enhanced by Agentic AI. At the large spatial scale, a log-linear learning based EPG (L3-EPG) algorithm is developed to optimize inter-UAV link configurations, enabling sparse yet connected network topologies while reducing redundant links and interference. At the small spatial scale, an approximate gradient based EPG (AG-EPG) algorithm jointly optimizes UAV deployment, transmission power allocation, and ground user (GU) association to improve network throughput and latency. To further enhance adaptability across heterogeneous scenarios, a large language model (LLM) is incorporated as a knowledge-driven decision enhancer to automatically generate utility weights according to network characteristics, alleviating reliance on manual parameter tuning. Simulation results demonstrate that the proposed framework consistently outperforms baseline methods in terms of energy consumption, end-to-end latency, and system throughput.