Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence
Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …
powerful modeling capabilities and have been successfully applied in natural language …
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 …
Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …
important for network optimization. The current 5G and conceived 6G network in the future …
Wi-Fi meets ML: A survey on improving IEEE 802.11 performance with machine learning
Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant
position in providing Internet access thanks to their freedom of deployment and configuration …
position in providing Internet access thanks to their freedom of deployment and configuration …
Machine learning in beyond 5G/6G networks—State-of-the-art and future trends
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important
role in realizing and optimizing 6G network applications. In this paper, we present a brief …
role in realizing and optimizing 6G network applications. In this paper, we present a brief …
Unfolding WMMSE using graph neural networks for efficient power allocation
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
In solving this problem, we depart from classical purely model-based approaches and …
In solving this problem, we depart from classical purely model-based approaches and …
An overview on the application of graph neural networks in wireless networks
In recent years, with the rapid enhancement of computing power, deep learning methods
have been widely applied in wireless networks and achieved impressive performance. To …
have been widely applied in wireless networks and achieved impressive performance. To …
Enabling AI in future wireless networks: A data life cycle perspective
Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT)
networks, which can be mostly attributed to the increasing communication and sensing …
networks, which can be mostly attributed to the increasing communication and sensing …
Cooperative trajectory design of multiple UAV base stations with heterogeneous graph neural networks
Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means
for tackling eruptive communication service requirements especially when terrestrial …
for tackling eruptive communication service requirements especially when terrestrial …
Cache-aided MEC for IoT: Resource allocation using deep graph reinforcement learning
With the growing demand for latency-sensitive and compute-intensive services in the
Internet of Things (IoT), multiaccess edge computing (MEC)-enabled IoT is envisioned as a …
Internet of Things (IoT), multiaccess edge computing (MEC)-enabled IoT is envisioned as a …