Cellular traffic prediction with machine learning: A survey
W Jiang - Expert Systems with Applications, 2022 - Elsevier
Cellular networks are important for the success of modern communication systems, which
support billions of mobile users and devices. Powered by artificial intelligence techniques …
support billions of mobile users and devices. Powered by artificial intelligence techniques …
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 …
A graph convolution network‐deep reinforcement learning model for resilient water distribution network repair decisions
Water distribution networks (WDNs) are critical infrastructure for communities. The dramatic
expansion of the WDNs associated with urbanization makes them more vulnerable to high …
expansion of the WDNs associated with urbanization makes them more vulnerable to high …
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 …
A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue
J Xu, Y Wang, B Zhang, J Ma - Future Generation Computer Systems, 2024 - Elsevier
Abstract Software-Defined Network (SDN) paradigm decouples control plane from data
plane and provides a logically-centralized control to whole underlying network, which …
plane and provides a logically-centralized control to whole underlying network, which …
Energy-efficient resource allocation based on deep Q-network in V2V communications
D Han, J So - Sensors, 2023 - mdpi.com
Recently, with the development of autonomous driving technology, vehicle-to-everything
(V2X) communication technology that provides a wireless connection between vehicles …
(V2X) communication technology that provides a wireless connection between vehicles …
Graph-based resource allocation for integrated space and terrestrial communications
Resource allocation (RA) has always had a prominent place in wireless communications
research due to its significance for network throughput maximization, and its inherent …
research due to its significance for network throughput maximization, and its inherent …
Graph convolutional reinforcement learning for resource allocation in hybrid overlay–underlay cognitive radio network with network slicing
Nowadays, wireless communication system is facing the problems of spectrum resource
shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized …
shortage. Cognitive radio technology allows cognitive users to use the spectrums authorized …
Deep reinforcement learning for resource allocation with network slicing in cognitive radio network
S Yuan, Y Zhang, W Qie, T Ma, S Li - Computer Science and …, 2021 - doiserbia.nb.rs
With the development of wireless communication technology, the requirement for data rate is
growing rapidly. Mobile communication system faces the problem of shortage of spectrum …
growing rapidly. Mobile communication system faces the problem of shortage of spectrum …
Slice allocation of 5G network for smart grid with deep reinforcement learning ACKTR
L Zhong, J Hu, H Shen, C Xu… - 2022 7th International …, 2022 - ieeexplore.ieee.org
Smart grid is one of the representative applications for 5G network. In this scenario, different
business types of smart grid have diverse requirements in service quality, isolation level …
business types of smart grid have diverse requirements in service quality, isolation level …