Energy-efficient deep reinforcement learning assisted resource allocation for 5G-RAN slicing
One of the pillars of the 5G architecture is network slicing, in which hardware, radio, and
power resources are virtualized as a logical network taking into account the requirements of …
power resources are virtualized as a logical network taking into account the requirements of …
Revised reinforcement learning based on anchor graph hashing for autonomous cell activation in cloud-RANs
Cloud radio access networks (C-RANs) have been regarded in recent times as a promising
concept in future 5G technologies where all DSP processors are moved into a central base …
concept in future 5G technologies where all DSP processors are moved into a central base …
Deep reinforcement learning-based channel allocation for wireless lans with graph convolutional networks
K Nakashima, S Kamiya, K Ohtsu, K Yamamoto… - IEEE …, 2020 - ieeexplore.ieee.org
For densely deployed wireless local area networks (WLANs), this paper proposes a deep
reinforcement learning-based channel allocation scheme that enables the efficient use of …
reinforcement learning-based channel allocation scheme that enables the efficient use of …
Generalizable GNN-based 5G RAN/MEC Slicing and Admission control in metropolitan networks
The 5G RAN functions can be virtualized and distributed across the radio unit (RU),
distributed unit (DU), and centralized unit (CU) to facilitate flexible resource management …
distributed unit (DU), and centralized unit (CU) to facilitate flexible resource management …
On using deep reinforcement learning to dynamically derive 5G new radio TDD pattern
The deployment of 5G and 6G is highly motivated by the emerging network services that
demand more band-width and very low latency. Besides, these services are shifting from …
demand more band-width and very low latency. Besides, these services are shifting from …
Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …
Meta-gating framework for fast and continuous resource optimization in dynamic wireless environments
With the great success of deep learning (DL) in image classification, speech recognition,
and other fields, more and more studies have applied various neural networks (NNs) to …
and other fields, more and more studies have applied various neural networks (NNs) to …
ML-based radio resource management in 5G and beyond networks: A survey
In this survey, a comprehensive study is provided, regarding the use of machine learning
(ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) …
(ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) …
QoS-aware resource allocation of two-tier HetNet: A Q-learning approach
W AlSobhi, AH Aghvami - 2019 26th International Conference …, 2019 - ieeexplore.ieee.org
Data applications account for the magnitude of traffic generated in the Cellular Networks. To
meet the ever-increasing traffic demand, advancement in resource allocation is crucial …
meet the ever-increasing traffic demand, advancement in resource allocation is crucial …
Cooperative radio resource management in heterogeneous cloud radio access networks
Responding to the unprecedented challenges imposed by the 5G communications
ecosystem, emerging heterogeneous network architectures allow for improved integration …
ecosystem, emerging heterogeneous network architectures allow for improved integration …