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

## TL;DR

This paper proposes an optimization framework for UAV-assisted edge computing that jointly schedules resources and plans trajectories, significantly improving energy efficiency and performance for latency-critical tasks.

## Contribution

It introduces a novel joint optimization algorithm for UAV trajectory, resource scheduling, and bandwidth allocation in UAV-assisted MEC systems.

## Key findings

- Significant energy savings achieved compared to conventional methods.
- Enhanced performance for latency-critical, computation-intensive tasks.
- The proposed algorithm outperforms existing approaches in simulation results.

## Abstract

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.