Abstract Self-Organizing Network (SON) stands for a key concept characterizing the
behavior of the future mobile networks. The evolution of telecom infrastructures towards 5G …

Tabular data and images have been used from machine learning models as two diverse
types of inputs, in order to perform path loss predictions in urban areas. Different types of …

Machine learning (ML) is more and more used to address the challenges of managing the
physical layer of increasingly heterogeneous and complex optical networks. In this tutorial …

There is an increased exploration of the potential of wireless communication networks in the
automation of daily human tasks via the Internet of Things. Such implementations are only …

This paper studies the problem of predicting the spectrum efficiency (SE) for massive
multiple-input multipleoutput (MIMO) empowered 5G networks based on the reference …

In urban scenarios, network planning requires awareness of the notoriously complex
propagation environment by accounting for blocking, diffraction, and reflection on buildings …

The performance of machine learning (ML)-based path loss models relies heavily on the
data they use at their inputs. Feature engineering is, therefore, essential for the model's …

Beyond 5G/6G should provide services everywhere, and it is necessary to expand area
coverage and develop high-frequency bands from millimeter waves to terahertz waves …

The integration of small-scale Unmanned Aerial Vehicles (UAVs) into Intelligent
Transportation Systems (ITSs) will empower novel smart-city applications and services. After …

Motivated by the teleoperation and local map sharing vehicular communication use cases,
we investigate whether uplink throughput can be predicted by different machine learning …