Smart resource allocation for mobile edge computing: A deep reinforcement learning approach
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …
capabilities has brought about a proliferation of numerous complex and computation …
Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA
In recent years, computation offloading has become an effective way to overcome the
constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive …
constraints of mobile devices (MDs) by offloading delay-sensitive and computation-intensive …
iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …
shown amazing performance in solving complex problems to support the Internet of Things …
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 …
Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial internet of things
Nowadays, driven by the rapid development of smart mobile equipments and 5G network
technologies, the application scenarios of Internet of Things (IoT) technology are becoming …
technologies, the application scenarios of Internet of Things (IoT) technology are becoming …
Deep reinforcement learning based approach for online service placement and computation resource allocation in edge computing
Due to the urgent emergence of computation-intensive intelligent applications on end
devices, edge computing has been put forward as an extension of cloud computing, to …
devices, edge computing has been put forward as an extension of cloud computing, to …
Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment
With the popularity of smart mobile equipment, the amount of data requested by users is
growing rapidly. The traditional centralized processing method represented by the cloud …
growing rapidly. The traditional centralized processing method represented by the cloud …
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 …
[HTML][HTML] Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing
The rapid growth of mobile internet services has yielded a variety of computation-intensive
applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which …
applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which …
Task-driven resource assignment in mobile edge computing exploiting evolutionary computation
The IoT network allows IoT devices to communicate with other devices, applications, and
services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC …
services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC …
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