Agentic AI for Low-Altitude Semantic Wireless Networks: An Energy Efficient Design

Abstract

This letter addresses the energy efficiency issue in unmanned aerial vehicle (UAV)-assisted autonomous systems. We propose a framework for an agentic artificial intelligence (AI)-powered low-altitude semantic wireless network, that intelligently orchestrates a sense-communicate-decide-control workflow. A system-wide energy consumption minimization problem is formulated to enhance mission endurance. This problem holistically optimizes key operational variables, including UAV's location, semantic compression ratio, transmit power of the UAV and a mobile base station, and binary decision for AI inference task offloading, under stringent latency and quality-of-service constraints. To tackle the formulated mixed-integer non-convex problem, we develop a low-complexity algorithm which can obtain the globally optimal solution with two-dimensional search. Simulation results validate the effectiveness of our proposed design, demonstrating significant reductions in total energy consumption compared to conventional baseline approaches.