# UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization

arXiv: https://arxiv.org/abs/1812.02658 · HTML: https://mlsanity.com/paper/1812.02658/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks](https://mlsanity.com/paper/2107.04648/) (2021)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC](https://mlsanity.com/paper/2403.10927/) (2024)
- [Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction](https://mlsanity.com/paper/2512.20902/) (2025)
- [Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV](https://mlsanity.com/paper/2605.04436/) (2026)

## Abstract (arXiv)

In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. Numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.