mlsanityData to 7 Oct 2026

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

arXiv 2501.15928 ↗First posted 27 Jan 2025cs.NI cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 27 Jan 2025.

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7 of 15 references.

References · 7

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2019An Introduction to Variational Autoencoderscited by 997
  5. 2024DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approachcited by 17
  6. 2020Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networkscited by 12
  7. 2022Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learningcited by 10

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusioncited by 0
  2. 2025Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networkscited by 0

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  1. 2025Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning
  2. 2026Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
  3. 2024DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach
  4. 2025Large Language Model-enhanced Reinforcement Learning for Low-Altitude Economy Networking
  5. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches
  6. 2025Agentic AI Empowered Multi-UAV Trajectory Optimization in Low-Altitude Economy Networks ↗
  7. 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL
  8. 2024Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning