Contemporary advances in multi-access edge computing: A survey of fundamentals, architecture, technologies, deployment cases, security, challenges, and directions
With advancements of cloud technologies Multi-Access Edge Computing (MEC) emerged as
a remarkable edge-cloud technology to provide computing facilities to resource-restrained …
a remarkable edge-cloud technology to provide computing facilities to resource-restrained …
A comprehensive systematic review on machine learning application in the 5G-RAN architecture: Issues, challenges, and future directions
Abstract The fifth-generation (5G) network is considered a game-changing technology that
promises advanced connectivity for businesses and growth opportunities. To gain a …
promises advanced connectivity for businesses and growth opportunities. To gain a …
Network slicing based learning techniques for iov in 5g and beyond networks
The effects of transport development on people's lives are diverse, ranging from economy to
tourism, health care, etc. Great progress has been made in this area, which has led to the …
tourism, health care, etc. Great progress has been made in this area, which has led to the …
Reinforcement learning for radio resource management in ran slicing: A survey
Dynamic radio resource allocation to network slices in mobile networks is challenging due to
the diverse requirements of RAN slices and the dynamic environment of wireless networks …
the diverse requirements of RAN slices and the dynamic environment of wireless networks …
Toward an efficient and dynamic allocation of radio access network slicing resources for 5G era
X Chang, T Ji, R Zhu, Z Wu, C Li, Y Jiang - IEEE Access, 2023 - ieeexplore.ieee.org
With the development of 5G technology and Internet of Things (IoT), more and more devices
are connected through 5G wirelessly. Radio access network (RAN) slicing, as a key feature …
are connected through 5G wirelessly. Radio access network (RAN) slicing, as a key feature …
Accelerating reinforcement learning via predictive policy transfer in 6G RAN slicing
Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …
Open RAN: A concise overview
MS Wani, M Kretschmer, B Schröder… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
Open RAN has emerged as a transformative approach in the evolution of cellular networks,
addressing challenges posed by modern applications and high network density. By …
addressing challenges posed by modern applications and high network density. By …
[HTML][HTML] CoTwin: Collaborative improvement of digital twins enabled by blockchain
M García-Valls, AM Chirivella-Ciruelos - Future Generation Computer …, 2024 - Elsevier
Integrating digital twin technology in Cyber–Physical Systems and Internet of Things can
boost their intelligence. Given the current maturity of digital twin technology (yet in progress) …
boost their intelligence. Given the current maturity of digital twin technology (yet in progress) …
Deep Reinforcement Learning for Online Resource Allocation in Network Slicing
Network slicing is a key enabler of 5G and beyond networks to satisfy the diverse quality of
service (QoS) requirements of different services simultaneously. In network slicing, radio …
service (QoS) requirements of different services simultaneously. In network slicing, radio …
O-RAN-enabled Intelligent Network Slicing to Meet Service-Level Agreement (SLA)
Network slicing plays a critical role in enabling multiple virtualized and independent network
services to be created on top of a common physical network infrastructure. In this paper, we …
services to be created on top of a common physical network infrastructure. In this paper, we …