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The active growth and dynamic nature of cellular networks makes network troubleshooting challenging. Identification of network problems leveraging on machine learning has gained a lot of visibility in the past few years, resulting in dramatically improved cellular network services. In this paper, we present a novel methodology to automate the fault identification process in a cellular network and to classify network anomalies, which combines supervised and unsupervised machine learning algorithms. Our experiments using real data from operational commercial mobile networks obtained through drive-test measurements as well as via the MONROE platform show that our method can automatically identify and classify networking anomalies, thus enabling timely and precise troubleshooting actions.
Wireless communication is a basis of the vision of connected and automated vehicles (CAVs). Given the heterogeneity of both wireless communication technologies and CAV applications, one question that is critical to technology road-mapping and policy
Enabling the integration of aerial mobile users into existing cellular networks would make possible a number of promising applications. However, current cellular networks have not been designed to serve aerial users, and hence an exploration of desig
With the seamless coverage of wireless cellular networks in modern society, it is interesting to consider the shape of wireless cellular coverage. Is the shape a regular hexagon, an irregular polygon, or another complex geometrical shape? Based on fr
Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process. The increasing volume of data accumulated in clinical trials provides a unique opportunity to disc
It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To eval