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

arXiv: https://arxiv.org/abs/2512.11862 · HTML: https://mlsanity.com/paper/2512.11862/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 58 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 18 of 18)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Heterogeneous-Agent Reinforcement Learning](https://mlsanity.com/paper/2304.09870/) (2023)
- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective](https://mlsanity.com/paper/2310.01036/) (2023)
- [DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach](https://mlsanity.com/paper/2406.06986/) (2024)
- [Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08914/) (2024)
- [Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G](https://mlsanity.com/paper/2303.01633/) (2023)
- [A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster Rescue](https://mlsanity.com/paper/2211.12988/) (2022)
- [Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05824/) (2025)
- [From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks](https://mlsanity.com/paper/2506.12308/) (2025)
- [Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework](https://mlsanity.com/paper/2405.11281/) (2024)
- [Distributionally Robust Optimization for Aerial Multi-access Edge Computing via Cooperation of UAVs and HAPs](https://mlsanity.com/paper/2506.01972/) (2025)
- [Energy-Constrained UAV Data Collection Systems: NOMA and OMA](https://mlsanity.com/paper/1910.13630/) (2019)
- [Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vessels](https://mlsanity.com/paper/2506.15225/) (2025)
- [Cooperative ISAC-empowered Low-Altitude Economy](https://mlsanity.com/paper/2412.20371/) (2024)
- [NFV-Enabled Service Recovery in Space-Air-Ground Integrated Networks: A Matching Game Based Approach](https://mlsanity.com/paper/2502.02141/) (2025)
- [Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2502.10731/) (2025)
- [Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion](https://mlsanity.com/paper/2505.23266/) (2025)

## Built on by (most cited first, 1 of 1)

- [Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks](https://mlsanity.com/paper/2602.23667/) (2026)

## Abstract (arXiv)

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.