Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL

2025/12/05 by Jiahao You, You, Jiahao, Ziye Jia +9
Computer Science · Engineering · #Adaptability #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Base station #Convergence (economics) #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Key (lock) #Machine Learning (cs.LG) #Markov decision process #Optimization problem #Reinforcement learning #Task (project management) #Trajectory #Trajectory optimization #UAV Applications and Optimization

paper · pdf · doi:10.48550/arxiv.2512.11862

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/12/05 · openalex created_date 2025/12/17 · openalex updated_date 2026/08/05

Abstract

The low-altitude intelligent networks (LAINs) emerge as a promising architecture for delivering low-latency and energy-efficient edge intelligence in dynamic and infrastructure-limited environments. By integrating unmanned aerial vehicles (UAVs), aerial base stations, and terrestrial base stations, LAINs can support mission-critical applications such as disaster response, environmental monitoring, and real-time sensing. However, these systems face key challenges, including energy-constrained UAVs, stochastic task arrivals, and heterogeneous computing resources. To address these issues, we propose an integrated air-ground collaborative network and formulate a time-dependent integer nonlinear programming problem that jointly optimizes UAV trajectory planning and task offloading decisions. The problem is challenging to solve due to temporal coupling among decision variables. Therefore, we design a hierarchical learning framework with two timescales. At the large timescale, a Vickrey-Clarke-Groves auction mechanism enables the energy-aware and incentive-compatible trajectory assignment. At the small timescale, we propose the diffusion-heterogeneous-agent proximal policy optimization, a generative multi-agent reinforcement learning algorithm that embeds latent diffusion models into actor networks. Each UAV samples actions from a Gaussian prior and refines them via observation-conditioned denoising, enhancing adaptability and policy diversity. Extensive simulations show that our framework outperforms baselines in energy efficiency, task success rate, and convergence performance.

Citations

• Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vessels
• From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks
• Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion
• Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs
• Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks
• Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
• NFV-Enabled Service Recovery in Space-Air-Ground Integrated Networks: A Matching Game Based Approach
• Cooperative ISAC-empowered Low-Altitude Economy
• Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
• Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework
• Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective
• Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization
• Heterogeneous-Agent Reinforcement Learning
• A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue
• High-Resolution Image Synthesis with Latent Diffusion Models
• UAV-Enabled Wireless Power Transfer: Trajectory Design and Energy Optimization

Related

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• Agentic AI Empowered Multi-UAV Trajectory Optimization in Low-Altitude Economy Networks
• Decentralized Computation Offloading With Cooperative UAVs: Multi-Agent Deep Reinforcement Learning Perspective