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

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 …

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

Deep learning has recently emerged as a disruptive technology to solve challenging radio
resource management problems in wireless networks. However, the neural network …

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

The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …

Graph convolutional learning has led to many exciting discoveries in diverse areas.
However, in some applications, traditional graphs are insufficient to capture the structure …

Graph neural network (GNN) is an efficient neural network model for graph data and is
widely used in different fields, including wireless communications. Different from other …

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

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …