Clustering and Reinforcement Learning based Multi-Access Edge Computing in Ultra Dense Networks
VN Udupa, VK Tumuluru - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Multi-access edge computing (MEC) and Ultra-dense networks (UDN) are a special case of
5G cellular networks where the density of base stations is higher compared to that of the end …
5G cellular networks where the density of base stations is higher compared to that of the end …
Toward reinforcement-learning-based service deployment of 5G mobile edge computing with request-aware scheduling
5G wireless network technology will not only significantly increase bandwidth but also
introduce new features such as mMTC and URLLC. However, high request latency will …
introduce new features such as mMTC and URLLC. However, high request latency will …
Reinforcement learning framework for server placement and workload allocation in multiaccess edge computing
A Mazloomi, H Sami, J Bentahar… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Cloud computing is a reliable solution to provide distributed computation power. However,
real-time response is still challenging regarding the enormous amount of data generated by …
real-time response is still challenging regarding the enormous amount of data generated by …
AI-enabled task offloading for improving quality of computational experience in ultra dense networks
Multi-access edge computing (MEC) and ultra-dense networking (UDN) are recognized as
two promising paradigms for future mobile networks that can be utilized to improve the …
two promising paradigms for future mobile networks that can be utilized to improve the …
QL-PPTS: A Machine Learning Scheduling Scheme on Edge Computing Environments
Abstract Multiple Access Edge computing (MEC) is a computing model that can extend cloud
computing and storage capabilities to the edge of cellular networks, close to IoT devices …
computing and storage capabilities to the edge of cellular networks, close to IoT devices …
Computing offloading and resource scheduling based on DDPG in ultra-dense edge computing networks
R Du, J Wang, Y Gao - The Journal of Supercomputing, 2024 - Springer
To address the current challenge of smart devices in healthcare Internet of things (IoT)
struggling to efficiently process intensive applications in real-time, a collaborative cloud …
struggling to efficiently process intensive applications in real-time, a collaborative cloud …
Mobile edge computing based task offloading and resource allocation in 5G ultra-dense networks
X Chen, Z Liu, Y Chen, Z Li - IEEE Access, 2019 - ieeexplore.ieee.org
Driven by the vision of 5G communication, the demand for mobile communication services
has increased explosively. Ultra-dense networks (UDN) is a key technology in 5G. The …
has increased explosively. Ultra-dense networks (UDN) is a key technology in 5G. The …
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 …
Online task offloading in udn: A deep reinforcement learning approach with incomplete information
Multi-access edge computing (MEC) and ultra-dense networking (UDN) are recognized as
two promising paradigms for future mobile networks that can be utilized to improve the …
two promising paradigms for future mobile networks that can be utilized to improve the …
Delay-aware joint resource allocation in cell-free mobile edge computing
This paper investigates a joint resource allocation problem in cell-free mobile edge
computing system which intends to minimize the number of users subjected to outage, due …
computing system which intends to minimize the number of users subjected to outage, due …