Dynamic offloading for multiuser muti-CAP MEC networks: A deep reinforcement learning approach
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 …
Joint computation offloading and task caching for multi-user and multi-task MEC systems: reinforcement learning-based algorithms
IA Elgendy, WZ Zhang, H He, BB Gupta… - Wireless …, 2021 - Springer
Computation offloading at mobile edge computing (MEC) servers can mitigate the resource
limitation and reduce the communication latency for mobile devices. Thereby, in this study …
limitation and reduce the communication latency for mobile devices. Thereby, in this study …
Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning
MEC is an emerging paradigm that utilizes computing resources at the network edge to
deploy heterogeneous applications and services. In the MEC system, mobile users and …
deploy heterogeneous applications and services. In the MEC system, mobile users and …
[HTML][HTML] Decentralized computation offloading for multi-user mobile edge computing: A deep reinforcement learning approach
Z Chen, X Wang - EURASIP Journal on Wireless Communications and …, 2020 - Springer
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 …
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 …
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 …
Computation offloading in multi-access edge computing: A multi-task learning approach
Multi-access edge computing (MEC) has already shown great potential in enabling mobile
devices to bear the computation-intensive applications by offloading some computing jobs to …
devices to bear the computation-intensive applications by offloading some computing jobs to …
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 dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems
Mobile Edge Computing (MEC) is one of the most promising techniques for next-generation
wireless communication systems. In this paper, we study the problem of dynamic caching …
wireless communication systems. In this paper, we study the problem of dynamic caching …