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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.19791 |
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| _version_ | 1866912602467074048 |
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| author | Zhao, Zhouxiang Yi, Ran Cang, Yihan Jin, Boyang Yang, Zhaohui Chen, Mingzhe Huang, Chongwen Zhang, Zhaoyang |
| author_facet | Zhao, Zhouxiang Yi, Ran Cang, Yihan Jin, Boyang Yang, Zhaohui Chen, Mingzhe Huang, Chongwen Zhang, Zhaoyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19791 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic AI for Low-Altitude Semantic Wireless Networks: An Energy Efficient Design Zhao, Zhouxiang Yi, Ran Cang, Yihan Jin, Boyang Yang, Zhaohui Chen, Mingzhe Huang, Chongwen Zhang, Zhaoyang Information Theory 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. |
| title | Agentic AI for Low-Altitude Semantic Wireless Networks: An Energy Efficient Design |
| topic | Information Theory |
| url | https://arxiv.org/abs/2509.19791 |