https://hdl.handle.net/20.500.12761/1 2026-11-10T21:25:14Z https://hdl.handle.net/20.500.12761/2109 Exploring KPI Trade-offs in O-RAN Conflict Resolution with Deep Reinforcement Learning Simeone, Michele; Spinelli, Francesco; Boffetti, Enrico; Bazco-Nogueras, Antonio; Municio, Esteban; Mancuso, Vincenzo Open-Radio Access Network (ORAN) enables independently developed xApps, software plug-ins, to optimize different radio objectives through the Near-Real-Time RAN Intelligent Controller. Their simultaneous operation can, however, generate conflicting actions and trade-offs among network Key Performance Indicators (KPIs), operational cost, and user Quality of Experience (QoE). We investigate Deep Reinforcement Learning (DRL) as an adaptive conflict mediator that observes xApp proposals and learns continuous RAN-parameter adaptations. Its objective jointly accounts for QoE, network cost, and configuration safety. In an integrated multi-xApp scenario, the learned mediator reduces normalized total cost by approximately 40% compared with uncoordinated xApps, mainly by reducing QoE degradation while preserving admission success and fairness. 2026-10-01T00:00:00Z https://hdl.handle.net/20.500.12761/2108 Methodological advances in the Network Scale-up Method Díaz-Aranda, Sergio This thesis contributes to the development of the Network Scale-up Method (NSUM). This technique emerged as an alternative to traditional survey methods and estimates the size of a group from a population using Aggregated Relational Data (ARD), which consists of the number of contacts that belong to several groups. NSUM leverages the number of connections of the participants' surrounding neighborhood, effectively increasing the fraction of the network for which data are available. In addition, the use of ARD preserves the privacy of the participants and therefore increases participation and honesty, mitigating biases of traditional surveys. Due to the advantages of NSUM, it has motivated a wide range of applications in public health, epidemiology, the estimation of sensitive groups, and the quantification of disaster impacts. Although NSUM techniques have been applied to a wide range of problems of public relevance since their introduction nearly four decades ago, several important research challenges remain unresolved. This thesis is a compendium of publications that address these previous research questions through four complementary directions. The first direction is the empirical evaluation of existing NSUM techniques. The second research line is the development of the theoretical foundations of NSUM and ARD methods. The third direction is the proposal of new methods. Finally, this thesis extends the application of these techniques to a new domain. Together, these contributions are presented across the chapters of the thesis. Chapter 2 provides an exhaustive evaluation of the performance of most of the NSUM estimators through an extensive simulation environment. The empirical comparison reveals the impact of the biases, the design of the survey, and the sample size on the existing NSUM estimators. Chapter 3 develops a theoretical framework for analyzing the error of the NSUM estimators, providing error bounds under adversarial scenarios and random structures. Chapter 4 proposes a framework for estimating temporal trends from ARD and provides a theoretical comparison of its advantages over traditional survey methods. Chapter 5 proposes robust versions of existing NSUM estimators by replacing the sample means with alternative location estimators. These versions mitigate the sensitivity to anomalous responses due to the use of sample means in the traditional NSUM techniques. Chapter 6 extends the application of NSUM to estimating voting intention in electoral processes, outperforming several well-established polling institutions using smaller sample sizes. Chapter 7 implements NSUM to measure attributes of virtual networks used for content distribution, decentralized payment systems, and blockchain technologies. This framework estimates network attributes using only the information from a small number of nodes, considerably reducing the intrusiveness and computational cost of existing measurement techniques that require exhaustive identification of the entire network. 2026-10-14T00:00:00Z https://hdl.handle.net/20.500.12761/2107 A Unified Hybrid Terminal Integrating GNSS and LEO-PNT with 5G and LEO Signals of Opportunity: Consolidated System Design and Measured Performance Domínguez-Tijero, Enrique; Fidalgo, Javier; Meneses, Marcelo; Albert Pinto, Freddy; García, David; José Mata, Francisco; Giustiniano, Domenico; Santaromita, Giuseppe; Otim, Timothy; Pigato, Francesco; Reus, Antoni; Fernández, Marc; Liu, Qi; Fabra, Fran; Seco-Granados, Gonzalo; López-Salcedo, José A. Positioning, Navigation and Timing (PNT) is evolving beyond conventional Global Navigation Satellite Systems (GNSS) through the use of complementary terrestrial and space-based signals. Dedicated Low Earth Orbit PNT (LEO-PNT), 5G New Radio (NR) terrestrial network (TN) signals, 5G non-terrestrial networks (NTN) signals (evaluated through controlled emulation of LEO-like delay and Doppler conditions), and LEO Signals of Opportunity (LEO-SOP) offer complementary geometry, availability, bandwidth and resilience. Their integration within a common receiver architecture can improve navigation performance in environments where GNSS alone is insufficient. 2026-09-01T00:00:00Z https://hdl.handle.net/20.500.12761/2106 MiniRetro: VLC Backscatter System for Miniaturized and Sustainable Internet of Things Talavante, Javier; Genovés Guzmán, Borja; Giustiniano, Domenico The proliferation of Internet of Things (IoT) devices demands communication technologies that are sustainable, low-power, and compact. While radio-frequency (RF) backscatter has enabled 𝜇��W-level operation, its miniaturization is constrained by the physical limits of RF antennas. Visible light communication (VLC) backscatter offers an alternative by reusing existing lighting infrastructure, yet current designs require a significant area and power consumption at the IoT device. In this paper we present MiniRetro, a miniaturized VLC backscatter module with an active retroreflective area of only 0.25 cm2, combining retroreflective sheetings with liquid crystal (LC) shutters. We model the VLC backscatter channel including the impact of retroreflector type, geometry, and photodiode (PD) placement, determining the configuration that maximizes the reflected signal strength. We also provide the first analytical and experimental framework characterizing the interaction between shutter and retroreflector, capturing polarization and transmittance losses and showing that the relative rotation angle can alter reflected power by up to 66%. We validate these features experimentally with a single-pixel VLC backscatter module, confirming a reliable meter-scale mobile communication while being the first VLC backscatter design to operate with a sub-cm2 active area. Finally, we show its implementation on a small-sized aquatic robot providing uplink connectivity for aquaponics monitoring. Our results demonstrate the feasibility of MiniRetro as a miniaturized and sustainable VLC backscatter system for next-generation IoT connectivity 2026-12-01T00:00:00Z