Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Zhao, Zhouxiang, Yi, Ran, Cang, Yihan, Jin, Boyang, Yang, Zhaohui, Chen, Mingzhe, Huang, Chongwen, Zhang, Zhaoyang
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083290735
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