Resource allocation in information-centric wireless networking with D2D-enabled MEC: A deep reinforcement learning approach
Recently, information-centric wireless networks (ICWNs) have become a promising Internet
architecture of the next generation, which allows network nodes to have computing and …
architecture of the next generation, which allows network nodes to have computing and …
Use What You Know: Network and Service Coordination Beyond Certainty
Modern services often comprise several components, such as chained virtual network
functions, microservices, or machine learning functions. Providing such services requires to …
functions, microservices, or machine learning functions. Providing such services requires to …
User allocation in mobile edge computing: A deep reinforcement learning approach
SP Panda, A Banerjee… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
In recent times, the need for low latency has made it necessary to deploy application
services physically and logically close to the users rather than using the cloud for hosting …
services physically and logically close to the users rather than using the cloud for hosting …
Reinforcement learning based multi-attribute slice admission control for next-generation networks in a dynamic pricing environment
Next-generation networks will provide intelligent infrastructure and management using
machine learning. In real-world applications, demand for resources and performance within …
machine learning. In real-world applications, demand for resources and performance within …
Adaptive request scheduling and service caching for MEC-assisted IoT networks: An online learning approach
D Ren, X Gui, K Zhang - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Multiaccess edge computing (MEC) is a new paradigm to meet the demand of resource-
hungry and latency-sensitive services by enabling the placement of services and execution …
hungry and latency-sensitive services by enabling the placement of services and execution …
Toward Reinforcement-Learning-Based Intelligent Network Control in 6G Networks
Reinforcement learning (RL) is a critical enabler for optimizing performance, automating the
deployment, and increasing the intelligence level of 6G networks. In this article, we first …
deployment, and increasing the intelligence level of 6G networks. In this article, we first …
From design to deployment of zero touch deep reinforcement learning WLANs
O Iacoboaiea, J Krolikowski, ZB Houidi… - IEEE Communications …, 2022 - ieeexplore.ieee.org
Machine learning is increasingly used to automate networking tasks, in a paradigm known
as zero touch network and service management (ZSM). In particular, deep reinforcement …
as zero touch network and service management (ZSM). In particular, deep reinforcement …
VERA: Resource orchestration for virtualized services at the edge
The combination of service virtualization and edge computing allows mobile users to enjoy
low latency services, while keeping data storage and processing local. However, the …
low latency services, while keeping data storage and processing local. However, the …
Deep reinforcement learning for edge computing and resource allocation in 5G beyond
Y Dai, D Xu, K Zhang, Y Lu… - 2019 IEEE 19th …, 2019 - ieeexplore.ieee.org
By extending computation capacity to the edge of wireless networks, edge computing has
the potential to enable computation-intensive and delay-sensitive applications in 5G and …
the potential to enable computation-intensive and delay-sensitive applications in 5G and …
Deep reinforcement learning-based task offloading and resource allocation in MEC-enabled wireless networks
SB Engidayehu, T Mahboob… - 2022 27th Asia Pacific …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) has recently become an enabling technology for mobile
operators that are offering a diverse set of services. These services require extensive …
operators that are offering a diverse set of services. These services require extensive …