Deep reinforcement learning based computation offloading and resource allocation for MEC
Mobile edge computing (MEC) has the potential to enable computation-intensive
applications in 5G networks. MEC can extend the computational capacity at the edge of …
applications in 5G networks. MEC can extend the computational capacity at the edge of …
Performance optimization in mobile-edge computing via deep reinforcement learning
To improve the quality of computation experience for mobile devices, mobile-edge
computing (MEC) is emerging as a promising paradigm by providing computing capabilities …
computing (MEC) is emerging as a promising paradigm by providing computing capabilities …
Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing
Mobile-edge computing (MEC) has emerged as a promising computing paradigm in the 5G
architecture, which can empower user equipments (UEs) with computation and energy …
architecture, which can empower user equipments (UEs) with computation and energy …
Joint server selection, cooperative offloading and handover in multi-access edge computing wireless network: A deep reinforcement learning approach
Multi-access edge computing (MEC) is the key enabling technology that supports compute-
intensive applications in 5G networks. By deploying powerful servers at the edge of wireless …
intensive applications in 5G networks. By deploying powerful servers at the edge of wireless …
Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning
To improve the quality of computation experience for mobile devices, mobile-edge
computing (MEC) is a promising paradigm by providing computing capabilities in close …
computing (MEC) is a promising paradigm by providing computing capabilities in close …
[HTML][HTML] Decentralized computation offloading for multi-user mobile edge computing: A deep reinforcement learning approach
Z Chen, X Wang - EURASIP Journal on Wireless …, 2020 - jwcn-eurasipjournals.springeropen …
Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-
limited mobile devices from computation-intensive tasks, which enables devices to offload …
limited mobile devices from computation-intensive tasks, which enables devices to offload …
Dynamic offloading for multiuser muti-CAP MEC networks: A deep reinforcement learning approach
C Li, J Xia, F Liu, D Li, L Fan… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
In this paper, we study a multiuser mobile edge computing (MEC) network, where tasks from
users can be partially offloaded to multiple computational access points (CAPs). We …
users can be partially offloaded to multiple computational access points (CAPs). We …
Efficient computation offloading for multi-access edge computing in 5G HetNets
To meet the mobile devices' surging demands for throughput and computation resources in
5G networks, heterogeneous networks (HetNets) and multi-access edge computing (MEC) …
5G networks, heterogeneous networks (HetNets) and multi-access edge computing (MEC) …
Lyapunov-guided deep reinforcement learning for stable online computation offloading in mobile-edge computing networks
Opportunistic computation offloading is an effective method to improve the computation
performance of mobile-edge computing (MEC) networks under dynamic edge environment …
performance of mobile-edge computing (MEC) networks under dynamic edge environment …
Learning for computation offloading in mobile edge computing
Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users
(MUs) at the edge of wireless networks. However, deploying MEC systems faces many …
(MUs) at the edge of wireless networks. However, deploying MEC systems faces many …