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 …

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 …

Graph neural networks (GNNs) have been developed to solve challenging resource
allocation (RA) problems, which leads to hopeful results in small and simple communication …

The importance of an efficient network resource allocation strategy has grown significantly
with the rapid advancement of cellular network technology and the widespread use of …

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

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 …

Machine learning (ML) has been widely used for efficient resource allocation (RA) in
wireless networks. Although superb performance is achieved on small and simple networks …

With the development of wireless communications, heterogeneous ultra-dense networks
(HUDNs) have emerged timely to meet the requirements of massive connectivity, high data …

Graph Neural Networks (GNNs) have recently emerged as a promising approach to tackling
power allocation problems in wireless networks. Since unpaired transmitters and receivers …