Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913666912223232 |
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| author | Liu, Zhang Niyato, Dusit Wang, Jiacheng Sun, Geng Huang, Lianfen Gao, Zhibin Wang, Xianbin |
| author_facet | Liu, Zhang Niyato, Dusit Wang, Jiacheng Sun, Geng Huang, Lianfen Gao, Zhibin Wang, Xianbin |
| contents | Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15928 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking Liu, Zhang Niyato, Dusit Wang, Jiacheng Sun, Geng Huang, Lianfen Gao, Zhibin Wang, Xianbin Networking and Internet Architecture Artificial Intelligence Machine Learning Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term decisions while ensuring system stability. This theory is particularly valuable in unmanned aerial vehicle (UAV)-based low-altitude economy (LAE) networking scenarios, where it could effectively address inherent challenges of dynamic network conditions, multiple optimization objectives, and stability requirements. Recently, generative artificial intelligence (GenAI) has garnered significant attention for its unprecedented capability to generate diverse digital content. Extending beyond content generation, in this paper, we propose a framework integrating generative diffusion models with reinforcement learning to address Lyapunov optimization problems in UAV-based LAE networking. We begin by introducing the fundamentals of Lyapunov optimization theory and analyzing the limitations of both conventional methods and traditional AI-enabled approaches. We then examine various GenAI models and comprehensively analyze their potential contributions to Lyapunov optimization. Subsequently, we develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study. Finally, we outline several directions for future research. |
| title | Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.15928 |