# Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model

arXiv: https://arxiv.org/abs/2405.01555 · HTML: https://mlsanity.com/paper/2405.01555/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [AI-Generated Network Design: A Diffusion Model-based Learning Approach](https://mlsanity.com/paper/2303.13869/) (2023)
- [FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing](https://mlsanity.com/paper/2305.01536/) (2023)
- [Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study](https://mlsanity.com/paper/2309.01065/) (2023)
- [Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks](https://mlsanity.com/paper/2210.16797/) (2022)
- [Offloading Deep Learning Powered Vision Tasks from UAV to 5G Edge Server with Denoising](https://mlsanity.com/paper/2302.01991/) (2023)
- [Coalitional Game Framework for Content Distribution Using Device-to-device Communication](https://mlsanity.com/paper/1912.06975/) (2019)

## Built on by (most cited first, 3 of 3)

- [Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach](https://mlsanity.com/paper/2407.00978/) (2024)
- [Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.13554/) (2025)
- [Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach](https://mlsanity.com/paper/2510.24255/) (2025)

## Abstract (arXiv)

To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources.