Deep reinforcement learning‐based resource allocation in multi‐access edge computing
Network architects and engineers face challenges in meeting the increasing complexity and
low‐latency requirements of various services. To tackle these challenges, multi‐access …
low‐latency requirements of various services. To tackle these challenges, multi‐access …
Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
Latency and energy aware rate maximization in MC-NOMA-based multi-access edge computing: A two-stage deep reinforcement learning approach
M Nduwayezu, JH Yun - Computer Networks, 2022 - Elsevier
Future network services are emerging with an inevitable need for high wireless capacity
along with strong computational capabilities, stringent latency and reduced energy …
along with strong computational capabilities, stringent latency and reduced energy …
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 …
Mobility-aware resource allocation in multi-access edge computing using deep reinforcement learning
N Din, H Chen, D Khan - 2019 IEEE Intl Conf on Parallel & …, 2019 - ieeexplore.ieee.org
Mobile Edge Computing (also known as Multi-access Edge Computing) brings computation
and storage resources to the edge of a mobile network, allowing mobile devices (MDs) to …
and storage resources to the edge of a mobile network, allowing mobile devices (MDs) to …
Task computation offloading for multi-access edge computing via attention communication deep reinforcement learning
This article investigates how to enhance the Multi-access Edge Computing (MEC) systems
performance with the aid of device-to-device (D2D) communication computation offloading …
performance with the aid of device-to-device (D2D) communication computation offloading …
A deep reinforcement learning-based resource management scheme for SDN-MEC-supported XR applications
B Trinh, GM Muntean - 2022 IEEE 19th Annual Consumer …, 2022 - ieeexplore.ieee.org
The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for
efficient computing services at edge nodes, such as base stations (BS), access points (AP) …
efficient computing services at edge nodes, such as base stations (BS), access points (AP) …
Deep Reinforcement Learning for Performance‐Aware Adaptive Resource Allocation in Mobile Edge Computing
Mobile edge computing (MEC) enables to provide relatively rich computing resources in
close proximity to mobile users, which enables resource‐limited mobile devices to offload …
close proximity to mobile users, which enables resource‐limited mobile devices to offload …
[HTML][HTML] Resource allocation in multi-access edge computing for 5G-and-beyond networks
Innovative services with strict requirements are expected in the fifth generation (5G) of
mobile networks and beyond. For example, the Ultra-Reliable Low-Latency Communication …
mobile networks and beyond. For example, the Ultra-Reliable Low-Latency Communication …
“DRL+ FL”: An intelligent resource allocation model based on deep reinforcement learning for mobile edge computing
N Shan, X Cui, Z Gao - Computer Communications, 2020 - Elsevier
With the emergence of a large number of computation-intensive and time-sensitive
applications, smart terminal devices with limited resources can only run the model training …
applications, smart terminal devices with limited resources can only run the model training …