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Preprint Jul 2026

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

A predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation and designs an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices.

M. Farhoudi, Zeinab Sasan, Masoud Shokrnezhad et al. · 0 citations
2026

Optimizing Information Freshness in Satellite-UAV IoRT Networks: A Heterogeneous Multi-Agent Approach

In satellite-UAV assisted communication networks, jointly optimizing the UAV’s trajectory and the multi-agent scheduling decisions to minimize the age of information (AoI) is a notoriously challenging problem. The complexity is compounded by the fundamental heterogeneity between the satellite and UAV agents, including...

Wei-Jie Zhou, Mengjie Yi, Yan Zhang et al. · 0 citations
2026

SkySched: A Hierarchical and Scalable Reinforcement Learning Framework for Multi-UAV Vehicular Edge Computing Network

Unmanned Aerial Vehicles (UAVs) are increasingly deployed as embodied aerial agents in low-altitude economies, forming mobile aerial edge networks that enable flexible computation offloading for vehicles. However, their limited endurance and frequent join/leave behaviours result in highly dynamic topologies, underminin...

Meng Yi, V. Lee, Miao Du et al. · 0 citations
2026

Agentic AI-Empowered Reliable Routing in Interference-Aware UAV Networks

The burgeoning low-altitude economies place stringent demands on reliable communication within uncrewed aerial vehicle (UAV) networks. However, in complex electromagnetic environments, the topology and communication link quality of highly maneuverable UAVs exhibit strong random fluctuations, leading to frequent routing...

Wenjing Wei, Tianyu Wang, Hanze Liu et al. · 0 citations
Jul 2026

Coverage-aware offloading in multi-UAV-aided terrestrial MEC networks using quantum-inspired particle swarm optimization

An MECN that integrates UAVs as the aerial layer and TESs as the terrestrial layer is introduced, and a quantum-inspired particle swarm optimization-based offloading strategy (QIPSO-TOS) is proposed to facilitate coverage-aware task offloading.

Marlom Bey, P. Kuila, Biswadip Bandyopadhyay et al. · 0 citations
Jul 2026

TRUAV: Distributed Multi-Agent Reinforcement Learning for Trajectory Planning and Routing Enhancement in UAV-Aided IoT-Enabled VANETs

Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory pla...

Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen et al. · 0 citations

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