Graph-based deep learning for communication networks: A survey
W Jiang - Computer Communications, 2022 - Elsevier
Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …
still many challenges that are not fully solved and new solutions are proposed continuously …
Graph neural networks for intelligent modelling in network management and orchestration: a survey on communications
The advancing applications based on machine learning and deep learning in
communication networks have been exponentially increasing in the system architectures of …
communication networks have been exponentially increasing in the system architectures of …
Cloud–edge collaborative SFC mapping for industrial IoT using deep reinforcement learning
S Xu, Y Li, S Guo, C Lei, D Liu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The industrial Internet of Things (IIoT) and 5G have been served as the key elements to
support the reliable and efficient operation of Industry 4.0. By integrating burgeoning …
support the reliable and efficient operation of Industry 4.0. By integrating burgeoning …
D-vine: Dynamic virtual network embedding in non-terrestrial networks
In this paper, we address the virtual network embedding (VNE) problem in non-terrestrial
networks (NTNs) enabling dynamic changes in the virtual network function (VNF) …
networks (NTNs) enabling dynamic changes in the virtual network function (VNF) …
A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches
This paper provides a comprehensive survey of the integration of graph neural networks
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …
Knowledge defined networks on the edge for service function chaining and reactive traffic steering
Emerging technologies such as network function virtualization and software-defined
networking (SDN) have made a phenomenal breakthrough in network management by …
networking (SDN) have made a phenomenal breakthrough in network management by …
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the
central network, incorporating edge nodes close to end devices. This expansion facilitates …
central network, incorporating edge nodes close to end devices. This expansion facilitates …
Service function chaining and traffic steering in SDN using graph neural network
In the network softwarization, Network Function Virtualisation (NFV) has shifted the standard
of network services deployment and management for telecommunication. However, the …
of network services deployment and management for telecommunication. However, the …
On using deep reinforcement learning for multi-domain SFC placement
Service Function Chaining (SFC) has emerged as a promising technology for 5G and
beyond. It leverages Network Function Virtualization (NFV) and Software Defined …
beyond. It leverages Network Function Virtualization (NFV) and Software Defined …
Hierarchical multi-agent deep reinforcement learning for SFC placement on multiple domains
Service Function Chaining (SFC) is the process of decomposing a network service into
multiple functions that successively process packets to deliver the end-to-end service. In a …
multiple functions that successively process packets to deliver the end-to-end service. In a …