This paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning.
Request PDF | On May 17, 2023, Tao Ren and others published FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC | Find, ...
Finally, MRL can significantly improve the training efficiency in learning new tasks and make the offloading algorithm more adaptive to the dynamic MEC.
FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC. Tao Ren (Institute of Software Chinese Academy of Sciences, China); ...
Oct 22, 2024 · A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts.
In this paper, we study the energy efficient task offloading and resource allocation problem for NOMA-enabled IoT in smart buildings and environment.
To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small ...
Missing: FEAT: Power Allocation
A novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system.
Missing: FEAT: | Show results with:FEAT:
This survey aims to present a thorough overview of current research efforts in MEC task offloading strategies, highlighting significant methodologies, ...
Missing: Fast | Show results with:Fast
In this paper, we propose an online control algorithm to maximize the long-term EE of a WPT-MEC system by making decisions on time allocations and transmission ...
Missing: Fast | Show results with:Fast