Multi-Agent Reinforcement Learning for SLA-Aware Network Slicing in UAV-Enabled MEC

Jul 16, 2026

UAV-enabled Mobile Edge Computing (MEC) supports flexible capacity provisioning for heterogeneous network slices, including HRLLC, eMBB, and mMTC. Ensuring slice-level SLA compliance under dynamic user mobility, stochastic task arrivals, and limited onboard energy and computing capacity remains a core challenge. The proposed work introduces a predictive multi-agent reinforcement learning framework that coordinates UAV trajectory control and computation resource allocation to proactively stabilize SLAs. A lightweight mobility prediction module forecasts near-future congestion and enables anticipatory repositioning. An SLA-aware reward function penalizes violation probability and duration across slices, plus total energy use. Agents are trained with MAPPO using centralized training and decentralized execution, and event-driven simulations with realistic traces show improved SLA stability with competitive delay and energy performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

Introduction

Background and motivation

Problem statement

Related work

© 2026 IEEE. Reprinting or republishing this material for the purpose of advertising or promotion, creating new collective works, reselling or redistributing to servers or lists, or using any copyrighted component in other works must adhere to IEEE policy. The paper has been accepted for publication in IEEE SoftCOM 2026 . Multi-Agent Reinforcement Learning for SLA-Aware Network Slicing in UAV-Enabled MEC Mohammad Farhoudi 1 , Zeinab Sasan2 , Masoud Shokrnezhad3 , and Tarik Taleb4 1 Oulu University, Oulu, Finland; [mohammad.farhoudi@oulu.fi](mohammad.farhoudi@oulu.fi) 2 Amirkabir University of Technology, Tehran, Iran; [z.sasan@aut.ac.ir](z.sasan@aut.ac.ir) 3 ICTFICIAL Oy, Espoo, Finland; [masoud.shokrnezhad@ictficial.com](masoud.shokrnezhad@ictficial.com) 4 Ruhr University Bochum (RUB), Bochum, Germany; [tarik.taleb@rub.de](tarik.taleb@rub.de) arXiv :2607 .09295v 1 [ cs .NI] 10 Jul 2026 Abstract—Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (MEC) offers flexible capacity provisioning for heterogeneous network slices, including Hyper-Reliable and Low-Latency Communication (HRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). However, guaranteeing slice-level Service-Level Agreements (SLAs) under dynamic user mobility, stochastic task arrivals, and constrained onboard energy and computing resources remains a fundamental challenge. This paper proposesa predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation. A lightweight prediction module forecasts near-future user mobility, enabling UAVs to anticipate congestion and reposition before SLA violations occur. We design an SLAaware reward function that explicitly penalizes both violation probability and duration across slices, alongside total energy consumption. UAV agents are trained using Multi-Agent Proximal Policy Optimization (MAPPO) with centralized training and decentralized execution, enabling scalable online decisionmaking. Event-driven simulations with realistic mobility traces demonstrate that the proposed framework significantly improves SLA stability compared with baselines while maintaining competitive energy efficiency and delay performance, approaching oracle-level performance with sufficiently accurate predictive information. Index Terms—UAV-enabled MEC, network slicing, SLA-aware resource allocation, multi-agent reinforcement learning, MAPPO. I. INTRODUCTION The rapid proliferation of computation-intensive and delaysensitive applications, such as augmented reality, autonomous systems, and real-time video analytics, has imposed stringent requirements on next-generation wireless networks [1] . Mobile Edge Computing (MEC) has emerged as a key enabler to address these challenges by bringing computational resources closer to end users, thereby reducing delay and alleviating backhaul congestion [2] . Meanwhile, Unmanned Aerial Vehicles (UAVs), due to their flexibility, rapid deployment, and communication capabilities, have been increasingly integrated into MEC systems to provide on-demand edge services in scenarios with limited or damaged infrastructure, such as remote monitoring and temporary hotspots. In parallel, network slicing by logically partitioning network resources into multiple isolated slices, enables customized service provisioning for applications with distinct performance requirements, such as Hyper Reliable and Low-Latency Communication (HRLLC), Enhanced Mobile Broadband (eMBB), and Massive MachineType Communication (mMTC) [3], [4] . The integration of UAV-enabled MEC with network slicing offers a promising paradigm for delivering flexible and efficient edge intelligence in dynamic environments. However, realizing this vision introduces significant technical challenges. In UAV-enabled MEC systems with network slicing, multiple UAVs should ser

FAQs

What problem does the proposed framework address in UAV-enabled MEC network slicing?

Guaranteeing slice-level SLA stability despite dynamic user mobility, stochastic task arrivals, and constrained onboard energy and computing resources.

How does the approach improve SLA stability before violations occur?

It uses a lightweight prediction module to forecast near-future user mobility, allowing UAVs to anticipate congestion and reposition proactively.

How are the multi-agent policies trained and executed?

Agents are trained with Multi-Agent Proximal Policy Optimization (MAPPO) using centralized training and decentralized execution, supporting scalable online decision-making.

Jul 16, 2026

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