A standard network selection and resource allocation mechanism in 5G heterogeneous networks using hybrid heuristic algorithm with multi-objective constraints

EURASIP Journal on Wireless Communications and Networking, Год журнала: 2025, Номер 2025(1)

Опубликована: Март 21, 2025

Язык: Английский

Energy-Efficient Multi-Agent Deep Reinforcement Learning Task Offloading and Resource Allocation for UAV Edge Computing

Sensors, Год журнала: 2025, Номер 25(11), С. 3403 - 3403

Опубликована: Май 28, 2025

The integration of Unmanned Aerial Vehicles (UAVs) into Mobile Edge Computing (MEC) systems has emerged as a transformative solution for latency-sensitive applications, leveraging UAVs’ unique advantages in mobility, flexible deployment, and on-demand service provisioning. This paper proposes novel multi-agent reinforcement learning framework, termed Multi-Agent Twin Delayed Deep Deterministic Policy Gradient Task Offloading Resource Allocation (MATD3-TORA), to optimize task offloading resource allocation UAV-assisted MEC networks. framework enables collaborative decision making among multiple UAVs efficiently serve sparsely distributed ground mobile devices (MDs) establish an integrated communication, computational model, which formulates joint optimization problem aimed at minimizing the weighted sum processing latency UAV energy consumption. Extensive experiments demonstrate that algorithm achieves improvements system efficiency compared conventional approaches. results highlight MATD3-TORA’s effectiveness addressing UAV-MEC challenges, including mobility–energy tradeoffs, making, real-time allocation.

Язык: Английский

Maximizing coverage in UAV-based emergency communication networks using deep reinforcement learning

Signal Processing, Год журнала: 2024, Номер 230, С. 109844 - 109844

Опубликована: Дек. 12, 2024

Язык: Английский