Deep learning with graph convolutional networks: An overview and latest applications in computational intelligence

UA Bhatti, H Tang, G Wu, S Marjan… - International Journal of …, 2023 - Wiley Online Library
Convolutional neural networks (CNNs) have received widespread attention due to their
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 …

Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption

F Tang, B Mao, Y Kawamoto… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
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 …

Wi-Fi meets ML: A survey on improving IEEE 802.11 performance with machine learning

S Szott, K Kosek-Szott, P Gawłowicz… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
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 …

Machine learning in beyond 5G/6G networks—State-of-the-art and future trends

VP Rekkas, S Sotiroudis, P Sarigiannidis, S Wan… - Electronics, 2021 - mdpi.com
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 …

Unfolding WMMSE using graph neural networks for efficient power allocation

A Chowdhury, G Verma, C Rao… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
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 …

An overview on the application of graph neural networks in wireless networks

S He, S Xiong, Y Ou, J Zhang, J Wang… - IEEE Open Journal …, 2021 - ieeexplore.ieee.org
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 …

Enabling AI in future wireless networks: A data life cycle perspective

DC Nguyen, P Cheng, M Ding… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
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 …

Cooperative trajectory design of multiple UAV base stations with heterogeneous graph neural networks

X Zhang, H Zhao, J Wei, C Yan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means
for tackling eruptive communication service requirements especially when terrestrial …

Cache-aided MEC for IoT: Resource allocation using deep graph reinforcement learning

D Wang, Y Bai, G Huang, B Song… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
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 …