Advancing RAN slicing with offline reinforcement learning
Dynamic radio resource management (RRM) in wireless networks presents significant
challenges, particularly in the context of Radio Access Network (RAN) slicing. This …
challenges, particularly in the context of Radio Access Network (RAN) slicing. This …
Toward safe and accelerated deep reinforcement learning for next-generation wireless networks
Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the
wireless networks domain. They are considered promising approaches for solving dynamic …
wireless networks domain. They are considered promising approaches for solving dynamic …
Designing future wireless networks (FWN) s with net zero (NZ) and zero touch (ZT) perspective
Recent research in Future Wireless Networks (FWN) s have primarily focused on improving
spectral and energy efficiency, emphasizing less on reducing power consumption. Studies …
spectral and energy efficiency, emphasizing less on reducing power consumption. Studies …
Network slicing via transfer learning aided distributed deep reinforcement learning
Deep reinforcement learning (DRL) has been in-creasingly employed to handle the dynamic
and complex re-source management in network slicing. The deployment of DRL policies in …
and complex re-source management in network slicing. The deployment of DRL policies in …
Mobility aware and energy-efficient federated deep reinforcement learning assisted resource allocation for 5G-RAN slicing
Network slicing is one of the foundations for the realization of 5G and beyond. However, due
to the mobility of the users and the network dynamics, flexible and efficient radio access …
to the mobility of the users and the network dynamics, flexible and efficient radio access …
Deep transfer reinforcement learning for resource allocation in hybrid multiple access systems
X Wang, Y Zhang, H Wu, T Liu, Y Xu - Physical Communication, 2022 - Elsevier
This paper proposes a resource allocation scheme for hybrid multiple access involving both
orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The …
orthogonal multiple access and non-orthogonal multiple access (NOMA) techniques. The …
Hierarchical Reinforcement Learning based Resource Allocation for RAN Slicing
As the complexity of wireless mobile networks increases significantly, artificial intelligence
(AI) and machine learning (ML) have become key enablers for radio resource management …
(AI) and machine learning (ML) have become key enablers for radio resource management …
Inter-Cell Network Slicing With Transfer Learning Empowered Multi-Agent Deep Reinforcement Learning
Network slicing enables operators to cost-efficiently support diverse applications on a
common physical infrastructure. The ever-increasing densification of network deployment …
common physical infrastructure. The ever-increasing densification of network deployment …
Traffic Classification using Deep Learning Approach for End-to-End Slice Management in 5G/B5G
NA Mohammedali, T Kanakis… - … on Information and …, 2022 - ieeexplore.ieee.org
Network slicing is a key role in future networks. 5G networks are intended to meet different
service demands of an application offered to users. 5G architecture is used to match the …
service demands of an application offered to users. 5G architecture is used to match the …
[图书][B] Adaptive Data-Driven Optimization Using Transfer Learning for Resilient, Energy-Efficient, Resource-Aware, and Secure Network Slicing in 5G-Advanced and …
A Thantharate - 2022 - search.proquest.com
Abstract 5G–Advanced is the next step in the evolution of the fifth–generation (5G)
technology. It will introduce a new level of expanded capabilities beyond connections and …
technology. It will introduce a new level of expanded capabilities beyond connections and …