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 …
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 …
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 …
Collaborative data caching and computation offloading for multi-service mobile edge computing
Mobile edge computing (MEC) can use wireless access network (RAN) to provide the
services required by user's information technology (IT) and cloud computing functions …
services required by user's information technology (IT) and cloud computing functions …
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 …
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 …
[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 …
Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing.
Y Wei, Z Wang, D Guo, FR Yu - Computers, Materials & …, 2019 - search.ebscohost.com
To reduce the transmission latency and mitigate the backhaul burden of the centralized
cloud-based network services, the mobile edge computing (MEC) has been drawing …
cloud-based network services, the mobile edge computing (MEC) has been drawing …
Multi-server multi-user multi-task computation offloading for mobile edge computing networks
This paper studies mobile edge computing (MEC) networks where multiple wireless devices
(WDs) offload their computation tasks to multiple edge servers and one cloud server …
(WDs) offload their computation tasks to multiple edge servers and one cloud server …