In wireless communications, transforming network into graphs and processing them using
deep learning models, such as Graph Neural Networks (GNNs), is one of the mainstream …

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 …

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep
learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models …

As an efficient graph analytical tool, graph neural networks (GNNs) have special properties
that are particularly fit for the characteristics and requirements of wireless communications …

Deep learning-based approaches have been developed to solve challenging problems in
wireless communications, leading to promising results. Early attempts adopted neural …

Graph neural networks (GNN) have shown outstanding applications in fields where data is
essentially represented as graphs (eg, chemistry, biology, and recommendation systems). In …

In wireless networks, the effectiveness of beamforming and power allocation strategies is
crucial in meeting the increasing data demands of users and ensuring rapid data …

Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …

Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Next-generation (xG) wireless networks, with their complex and dynamic nature, present
significant challenges to using traditional optimization techniques. Generative AI (GAI) …