Intelligent edge: Leveraging deep imitation learning for mobile edge computation offloading
In this work, we propose a new deep imitation learning (DIL)-driven edge-cloud computation
offloading framework for MEC networks. A key objective for the framework is to minimize the …
offloading framework for MEC networks. A key objective for the framework is to minimize the …
Meta-learning based dynamic computation task offloading for mobile edge computing networks
Deep learning-based algorithms provide a promising solution to efficiently generate
offloading decisions in mobile edge computing (MEC) networks. However, considering …
offloading decisions in mobile edge computing (MEC) networks. However, considering …
An efficient online computation offloading approach for large-scale mobile edge computing via deep reinforcement learning
Mobile edge computing (MEC) has been envisioned as a promising paradigm that could
effectively enhance the computational capacity of wireless user devices (WUDs) and quality …
effectively enhance the computational capacity of wireless user devices (WUDs) and quality …
Mobile edge computation offloading using game theory and reinforcement learning
Due to the ever-increasing popularity of resource-hungry and delay-constrained mobile
applications, the computation and storage capabilities of remote cloud has partially migrated …
applications, the computation and storage capabilities of remote cloud has partially migrated …
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 …
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 …
Reducing offloading latency for digital twin edge networks in 6G
W Sun, H Zhang, R Wang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
6G is envisioned to empower wireless communication and computation through the
digitalization and connectivity of everything, by establishing a digital representation of the …
digitalization and connectivity of everything, by establishing a digital representation of the …
Fast adaptive task offloading in edge computing based on meta reinforcement learning
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to
reduce network traffic and service latency. A fundamental problem in MEC is how to …
reduce network traffic and service latency. A fundamental problem in MEC is how to …
An integrated optimization-learning framework for online combinatorial computation offloading in MEC networks
Mobile edge computing (MEC) is a promising paradigm to accommodate the increasingly
prosperous delay-sensitive and computation-intensive applications in 5G systems. To …
prosperous delay-sensitive and computation-intensive applications in 5G systems. To …
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 …