This paper investigates the scalability ability of Graph Neural Network (GNN) for solving
resource allocation problems in wireless networks. Although GNNs are able to work on …

An unsupervised method is proposed for link activation in wireless networks by identifying
clusters of interfering users. A k-nearest neighbors interference graph is first defined for the …

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 …

The increased sophistication of mobile networks such as 5G and beyond, and the plethora
of devices and novel use cases to be supported by these networks, make of the already …

The IEEE 802.11 ax standard is designed to provide high-efficiency WLAN operating in
dense deployment, with a focus on increasing robustness and uplink transmission. IEEE …

This chapter analyzes how the artificial intelligence and machine learning algorithms can be
used to prosper edge computing. In the first section, we propose several key performance …

Machine learning has become successful in solving wireless interference management
problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish …

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 resurgence of deep learning techniques has brought forth fundamental changes to how
hard problems could be solved. It used to be held that the solutions to complex wireless …

This paper proposes a deep learning-based end-to-end system for the beamforming
problem of time-division duplexing (TDD) multiuser multiple input single output (MUMISO) …