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

2025-09-24

AI Summary

To address the low energy efficiency and stringent end-to-end latency and QoS constraints in low-altitude semantic wireless networks supporting UAV-assisted autonomous systems, this paper proposes an agent-based AI framework enabling closed-loop synergy among perception, communication, decision-making, and control. We introduce the first autonomous agent architecture tailored for low-altitude semantic communications, integrating semantic information processing with joint optimization of multi-dimensional resources. A mixed-integer non-convex optimization model is formulated, and a low-complexity two-dimensional search algorithm is designed to jointly optimize UAV trajectory, semantic compression ratio, transmit power, and AI task offloading strategy. Simulation results demonstrate that, under strict latency and QoS requirements, the proposed approach significantly reduces total system energy consumption and substantially enhances mission endurance.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Addresses energy efficiency in UAV-assisted autonomous systems
• Optimizes key operational variables under latency constraints
• Solves mixed-integer non-convex energy minimization problem

Innovation

• Agentic AI orchestrates sense-communicate-decide-control workflow
• Optimizes UAV location, semantic compression, and power jointly
• Solves optimization with low-complexity two-dimensional search algorithm