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 survey of recent advances in optimization methods for wireless communications
Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …
solution tool for the design of wireless communications systems. While optimization has …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mobile networks
The high-speed mobile networks offer great potentials to many future intelligent applications,
such as autonomous vehicles in smart transportation systems. Such networks provide the …
such as autonomous vehicles in smart transportation systems. Such networks provide the …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system
The advancement of Internet of Things (IoT) technologies leads to a wide penetration and
large-scale deployment of IoT systems across an entire city or even country. While IoT …
large-scale deployment of IoT systems across an entire city or even country. While IoT …
Learning task-oriented communication for edge inference: An information bottleneck approach
This paper investigates task-oriented communication for edge inference, where a low-end
edge device transmits the extracted feature vector of a local data sample to a powerful edge …
edge device transmits the extracted feature vector of a local data sample to a powerful edge …
Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Learning to reflect and to beamform for intelligent reflecting surface with implicit channel estimation
Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective
elements, is capable of enhancing the wireless propagation environment in a cellular …
elements, is capable of enhancing the wireless propagation environment in a cellular …
Graph neural networks for wireless communications: From theory to practice
Deep learning-based approaches have been developed to solve challenging problems in
wireless communications, leading to promising results. Early attempts adopted neural …
wireless communications, leading to promising results. Early attempts adopted neural …