Radio resource management for 5G and beyond: empowering next-generation networks

Conserva, Francesca (2025) Radio resource management for 5G and beyond: empowering next-generation networks, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Ingegneria elettronica, telecomunicazioni e tecnologie dell'informazione, 37 Ciclo.
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Abstract

Future society is set to become increasingly digitized, hyper-connected, and globally data-driven. As Fifth-Generation (5G) technology nears global standardization, attention has shifted to developing Beyond 5G (B5G). The research community is thoroughly exploring the drivers, requirements, and challenges shaping Sixth-Generation (6G) vision, with two themes emerging as especially prominent: the 3D Network (3DN) and Network Digital Twin (NDT) paradigms, which encompass the research activities presented in this thesis. The first part investigates the 3DN paradigm, focusing on integrating Unmanned Aerial Vehicles (UAVs) to support terrestrial infrastructure, particularly for Vehicle-to-Everything applications. As vehicles evolve into digital hubs powered by Artificial Intelligence (AI), the shift toward self-driving adds new requirements for autonomous sensing and communication. To meet these, optimized Radio Resources Assignment (RRA) strategies are proposed, enhancing vehicular users Quality of Experience. Advancing this research, an analytical framework is introduced, featuring a novel performance metric that jointly evaluates UAV beam coverage and vehicles capacity to meet uplink data demands. The model incorporates vehicle mobility, assessing its impact on RRA performance and balancing system design trade-offs to optimize resource allocation and ensure consistent service quality. The second part explores Machine Learning-empowered latency predictive frameworks in 5G Radio Access Networks. Here, cell-level Key Performance Indicators drive latency analysis and prediction, enabling Predictive Quality of Service and enhancing Zero-Touch Service Management. A key potential of these algorithms lies in their role within NDTs, allowing Mobile Network Operators to simulate and fine-tune network adjustments, ensuring resilient Radio Resource Management (RRM) without disrupting live operations. In summary, both research activities converge on a singular goal: empowering RRM to create robust, adaptable, and future-ready 6G networks.

Abstract
Tipologia del documento
Tesi di dottorato
Autore
Conserva, Francesca
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
37
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
5G, 6G, 3D Network, Unmanned Aerial Vehicles, Vehicle-to-Vehicle, Vehicular Networks, Radio Resource Management, Radio Resources Assignment, QoE, Mobile Networks, Cellular Networks, Key Performance Indicators, QoS, Latency, Latency-Reliability, Artificial Intelligence, Machine Learning
Data di discussione
4 Aprile 2025
URI

Altri metadati

Gestione del documento: Visualizza la tesi

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