Dynamic networks, text analysis and Gephi: the art math

In numerosi campi scientici l'analisi di network complessi ha portato molte recenti scoperte: in questa tesi abbiamo sperimentato questo approccio sul linguaggio umano, in particolare quello scritto, dove le parole non interagiscono in modo casuale. Abbiamo quindi inizialmente presentato misure capaci di estrapolare importanti strutture topologiche dai newtork linguistici(Degree, Strength, Entropia, . . .) ed esaminato il software usato per rappresentare e visualizzare i grafi (Gephi). In seguito abbiamo analizzato le differenti proprietà statistiche di uno stesso testo in varie sue forme (shuffolato, senza stopwords e senza parole con bassa frequenza): il nostro database contiene cinque libri di cinque autori vissuti nel XIX secolo. Abbiamo infine mostrato come certe misure siano importanti per distinguere un testo reale dalle sue versioni modificate e perché la distribuzione del Degree di un testo normale e di uno shuffolato abbiano lo stesso andamento. Questi risultati potranno essere utili nella sempre più attiva analisi di fenomeni linguistici come l'autorship attribution e il riconoscimento di testi shuffolati.

Trajectory Planning in UAV-Aided Wireless Networks Via Reinforcement Learning

This thesis is focused on the design of a flexible, dynamic and innovative telecommunication's system for future 6G applications on vehicular communications. The system is based on the development of drones acting as mobile base stations in an urban scenario to cope with the increasing traffic demand and avoid network's congestion conditions. In particular, the exploitation of Reinforcement Learning algorithms is used to let the drone learn autonomously how to behave in a scenario full of obstacles with the goal of tracking and serve the maximum number of moving vehicles, by at the same time, minimizing the energy consumed to perform its tasks. This project is an extraordinary opportunity to open the doors to a new way of applying and develop telecommunications in an urban scenario by mixing it to the rising world of the Artificial Intelligence.

Simulation of URLLC slicing with resource reconfiguration in 5G networks

This thesis seeks to analyse the performance of dynamic slice provisioning in a 5G metro network with the low latency and reliability guaranties. This elaborate highlight the comparison in terms of performance of two versions of a simulator developed in Python based on different models: the Exhaustive research model and Shortest Path First Fit (SPFF) model. It further presents the differences between the dedicated path protection and the shared path protection. This analysis is made through several simulations at different network conditions by varying networks resources and observing the network performances while comparing the 2 models mentioned above. A reconfiguration procedure was implemented on backup resources in the shortest path first fit in order to improve its performance with respect to the exhaustive research which is more optimised. Subsequently, several triggering events was implemented, for the reconfiguration. And a comparison is made between these different triggering events in terms blocking probability, bandwidth at link, capacity at each node, primary and backup bandwidth per slice and backup capacity per slice.