# Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion

arXiv: https://arxiv.org/abs/2506.21933 · HTML: https://mlsanity.com/paper/2506.21933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 54 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 23 of 23)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Generative AI based Secure Wireless Sensing for ISAC Networks](https://mlsanity.com/paper/2408.11398/) (2024)
- [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)
- [Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue](https://mlsanity.com/paper/2309.16709/) (2023)
- [DiffSG: A Generative Solver for Network Optimization with Diffusion Model](https://mlsanity.com/paper/2408.06701/) (2024)
- [Diffusion Models as Network Optimizers: Explorations and Analysis](https://mlsanity.com/paper/2411.00453/) (2024)
- [Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking](https://mlsanity.com/paper/2502.18118/) (2025)
- [Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/2404.15292/) (2024)
- [Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays](https://mlsanity.com/paper/2308.01511/) (2023)
- [GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks](https://mlsanity.com/paper/2412.08296/) (2024)
- [TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/2403.15828/) (2024)
- [AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations](https://mlsanity.com/paper/2406.13335/) (2024)
- [Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2412.10700/) (2024)
- [UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach](https://mlsanity.com/paper/2501.05742/) (2025)
- [Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2501.06410/) (2025)
- [Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2505.03230/) (2025)
- [Proactive Traffic Offloading in Dynamic Integrated Multi-Satellite Terrestrial Networks](https://mlsanity.com/paper/2205.04940/) (2022)
- [Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking](https://mlsanity.com/paper/2501.15928/) (2025)
- [UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2502.07388/) (2025)
- [Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning](https://mlsanity.com/paper/2505.15571/) (2025)
- [Cross-Problem Solving for Network Optimization: Is Problem-Aware Learning the Key?](https://mlsanity.com/paper/2505.05067/) (2025)

## Abstract (arXiv)

With the rapid development of the low-altitude economy, air-ground integrated multi-access edge computing (MEC) systems are facing increasing demands for real-time and intelligent task scheduling. In such systems, task offloading and resource allocation encounter multiple challenges, including node heterogeneity, unstable communication links, and dynamic task variations. To address these issues, this paper constructs a three-layer heterogeneous MEC system architecture for low-altitude economic networks, encompassing aerial and ground users as well as edge servers. The system is systematically modeled from the perspectives of communication channels, computational costs, and constraint conditions, and the joint optimization problem of offloading decisions and resource allocation is uniformly abstracted into a graph-structured modeling task. On this basis, we propose a graph attention diffusion-based solution generator (GADSG). This method integrates the contextual awareness of graph attention networks with the solution distribution learning capability of diffusion models, enabling joint modeling and optimization of discrete offloading variables and continuous resource allocation variables within a high-dimensional latent space. We construct multiple simulation datasets with varying scales and topologies. Extensive experiments demonstrate that the proposed GADSG model significantly outperforms existing baseline methods in terms of optimization performance, robustness, and generalization across task structures, showing strong potential for efficient task scheduling in dynamic and complex low-altitude economic network environments.