Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking

• URL: http://arxiv.org/abs/2501.15928v1
• Date: Mon, 27 Jan 2025 10:27:15 GMT
• Title: Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking
• Authors: Zhang Liu, Dusit Niyato, Jiacheng Wang, Geng Sun, Lianfen Huang, Zhibin Gao, Xianbin Wang,
• Abstract summary: We introduce the fundamentals of Lyapunov optimization theory and analyze the limitations of both conventional methods and traditional AI-enabled approaches. We develop a Lyapunov-guided generative diffusion model-based reinforcement learning framework and validate its effectiveness through a UAV-based LAE networking case study.
• Score: 49.225507258297725
• License:
• Abstract: 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.

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