# Joint optimization of system utility in UAV-enabled edge computing

## TL;DR

This paper introduces a new framework for optimizing the overall performance of UAV-assisted mobile edge computing networks by maximizing system utility.

## Contribution

The novel framework jointly optimizes system utility by considering both service provider revenue and user costs in UAV-enabled MEC networks.

## Key findings

- The proposed model outperforms traditional heuristic algorithms in system utility.
- The algorithm demonstrates robust convergence and reliability across different network setups.

## Abstract

This paper addresses the joint optimization of system utility in Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (MEC) networks. Unlike traditional approaches that optimize individual components, such as users, UAVs, or base stations, we propose a novel framework aimed at maximizing the overall system utility, which is defined as the difference between the total revenue of service providers (UAVs and base stations) and the total cost incurred by users. The proposed model incorporates realistic constraints, including limited computational resources and energy consumption, and formulates the problem as a Mixed-Integer Nonlinear Programming (MINLP) model. To solve this complex optimization problem, we develop an efficient algorithm that integrates the Block Successive Upper-Bound Minimization (BSUM) framework with heuristic methods, enabling the decomposition of the original problem into tractable subproblems that are solved iteratively. Simulation results demonstrate that the proposed approach significantly outperforms traditional heuristic algorithms in terms of system utility, while also exhibiting robust convergence and reliability across various network configurations. The results highlight the effectiveness of joint optimization in improving both the economic and operational efficiency of UAV-assisted MEC systems, providing a solid foundation for future research in network utility management.