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

2026-03-08

AI Summary

This work addresses the challenging mixed-integer nonconvex optimization problem in unmanned aerial vehicle (UAV) networks, where discrete link decisions and continuous deployment parameters are tightly coupled. To tackle this, the authors propose a dual-scale topology optimization framework: at the large scale, link structures are optimized via an exact potential game (EPG), while at the small scale, deployment, transmit power, and user association are jointly refined. Innovatively, a large language model (LLM) is integrated into the EPG framework to automatically generate utility weights tailored to heterogeneous scenarios. Two complementary algorithms—L3-EPG and AG-EPG—are designed to cooperatively solve the hybrid decision-making problem. Experimental results demonstrate that the proposed approach significantly outperforms baseline methods in terms of energy consumption, end-to-end latency, and system throughput, achieving efficient, low-interference, and highly connected dynamic UAV deployments.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Planning with Language Models
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Unmanned Aerial Vehicular Networks (UAVNs) are 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 autonomous 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 topology 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 topology 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.

Problem

• UAV Networks
• Topology Optimization
• Mixed-Integer Nonconvex Problem
• Dynamic Network Conditions
• Multi-Agent Coordination

Innovation

• Exact Potential Game
• Agentic AI
• Large Language Model
• UAV Network Deployment
• Dual Spatial-Scale Optimization