Optimizing power control in multi-cell cellular networks with deep learning enables such a
non-convex problem to be implemented in real-time. When channels are time-varying, the …

In multiuser communication systems, user scheduling and beamforming (US-BF) design are
two fundamental problems that are usually studied separately in the existing literature. In this …

We consider the problem of power control in wireless networks, consisting of multiple
transmitter-receiver pairs communicating with each other over a single shared wireless …

In this paper, we propose two novel geometric machine learning (G-ML) methods for the
wireless link scheduling problem in device-to-device (D2D) networks. In dynamic D2D …

Data-driven machine learning approaches have been proposed to facilitate wireless
network optimization by learning latent knowledge from historical optimization instances …

We consider the problem of binary power control, or link scheduling, in wireless interference
networks, where the power control policy is trained using graph representation learning. We …

This paper proposes a novel knowledge-driven approach for resource allocation in wireless
networks using the graph neural network (GNN) architecture. To meet the millisecond-level …

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

Consider the max-min fair multi-group multicast beamforming problem in wireless networks,
where the users with the same request are partitioned into a multicast group and served by …

We consider a class of non-convex complex modulus constrained problems (CMCPs), which
has many important applications in signal processing for wireless communications …