# Mobile Edge Computing for Cellular-Connected UAV: Computation Offloading and Trajectory Optimization

## TL;DR

This paper proposes an efficient joint optimization of UAV trajectory and computation offloading in mobile edge computing, significantly reducing mission completion time for UAVs served by cellular ground base stations.

## Contribution

It introduces a novel joint optimization framework for UAV trajectory and offloading scheduling in MEC, with an efficient algorithm for high-quality suboptimal solutions.

## Key findings

- Proposed algorithm reduces UAV mission completion time significantly.
- Joint optimization outperforms benchmark schemes.
- Numerical results validate the effectiveness of the approach.

## Abstract

This paper studies a new mobile edge computing (MEC) setup where an unmanned aerial vehicle (UAV) is served by cellular ground base stations (GBSs) for computation offloading. The UAV flies between a give pair of initial and final locations, during which it needs to accomplish certain computation tasks by offloading them to some selected GBSs along its trajectory for parallel execution. Under this setup, we aim to minimize the UAV's mission completion time by optimizing its trajectory jointly with the computation offloading scheduling, subject to the maximum speed constraint of the UAV, and the computation capacity constraints at GBSs. The joint UAV trajectory and computation offloading optimization problem is, however, non-convex and thus difficult to be solved optimally. To tackle this problem, we propose an efficient algorithm to obtain a high-quality suboptimal solution. Numerical results show that the proposed design significantly reduces the UAV's mission completion time, as compared to benchmark schemes.