arXiv subjects

Matteo Bordin

Publications and source records attributed to Matteo Bordin.

8 recordsLinked to original sources

arXiv2026

A measurement-based protocol for the generation of delocalised quantum states of a mechanical system

Non-Gaussian mechanical states are a key resource for quantum-enhanced sensing and tests of macroscopic quantum physics. We propose a measurement-based protocol to herald delocalized, nonclassical states of a mechanical oscillator in cavity optomechanics by conditioning on Geiger photodetection of the optical output. We analyse under which conditions Stokes-induced optomechanical entanglement give rise to mechanical Wigner Function negativity upon detection. We develop and compare a blue-detuned pulsed scheme and a continuous-wave steady-state scheme employing temporal-mode filtering, and we quantify heralding rates and robustness to finite temperature under realistic detection efficiencies.

quant-ph↗

arXiv2026

Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN

The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced and a tutorial for energy efficiency testing in ns-O-RAN.

cs.NI↗

arXiv2025

Enabling Site-Specific Cellular Network Simulation Through Ray-Tracing-Driven ns-3

Evaluating cellular systems, from 5G New Radio (NR) and 5G-Advanced to 6G, is challenging because the performance emerges from the tight coupling of propagation, beam management, scheduling, and higher-layer interactions. System-level simulation is therefore indispensable, yet the vast majority of studies rely on the statistical 3GPP channel models. These are well suited to capture average behavior across many statistical realizations, but cannot reproduce site-specific phenomena such as corner diffraction, street-canyon blockage, or deterministic line-of-sight conditions and angle-of-departure/arrival relationships that drive directional links. This paper extends 5G-LENA, an NR module for the system-level Network Simulator 3 (ns-3), with a trace-based channel model that processes the Multipath Components (MPCs) obtained from external ray-tracers (e.g., Sionna Ray Tracer (RT)) or measurement campaigns. Our module constructs frequency-domain channel matrices and feeds them to the existing Physical (PHY)/Medium Access Control (MAC) stack without any further modifications. The result is a geometry-based channel model that remains fully compatible with the standard 3GPP implementation in 5G-LENA, while delivering site-specific geometric fidelity. This new module provides a key building block toward Digital Twin (DT) capabilities by offering realistic site-specific channel modeling, unlocking studies that require site awareness, including beam management, blockage mitigation, and environment-aware sensing. We demonstrate its capabilities for precise beam-steering validation and end-to-end metric analysis. In both cases, the trace-driven engine exposes performance inflections that the statistical model does not exhibit, confirming its value for high-fidelity system-level cellular networks research and as a step toward DT applications.

cs.NI↗

arXiv2025

5G Aero: A Prototyping Platform for Evaluating Aerial 5G Communications

The application of small-factor, 5G-enabled Unmanned Aerial Vehicles (UAVs) has recently gained significant interest in various aerial and Industry 4.0 applications. However, ensuring reliable, high-throughput, and low-latency 5G communication in aerial applications remains a critical and underexplored problem. This paper presents the 5th generation (5G) Aero, a compact UAV optimized for 5G connectivity, aimed at fulfilling stringent 3rd Generation Partnership Project (3GPP) requirements. We conduct a set of experiments in an indoor environment, evaluating the UAV's ability to establish high-throughput, low-latency communications in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions. Our findings demonstrate that the 5G Aero meets the required 3GPP standards for Command and Control (C2) packets latency in both LoS and NLoS, and video latency in LoS communications and it maintains acceptable latency levels for video transmission in NLoS conditions. Additionally, we show that the 5G module installed on the UAV introduces a negligible 1% decrease in flight time, showing that 5G technologies can be integrated into commercial off-the-shelf UAVs with minimal impact on battery lifetime. This paper contributes to the literature by demonstrating the practical capabilities of current 5G networks to support advanced UAV operations in telecommunications, offering insights into potential enhancements and optimizations for UAV performance in 5G networks

cs.NI↗

arXiv2024

Design and Evaluation of Deep Reinforcement Learning for Energy Saving in Open RAN

Next-generation wireless systems, already widely deployed, are expected to become even more prevalent in the future, representing challenges in both environmental and economic terms. This paper focuses on improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems through the near-real-time dynamic activation and deactivation of Base Station (BS) Radio Frequency (RF) frontends using Deep Reinforcement Learning (DRL) algorithms, i.e., Proximal Policy Optimization (PPO) and Deep Q-Network (DQN). These algorithms run on the RAN Intelligent Controllers (RICs), part of the Open RAN architecture, and are designed to make optimal network-level decisions based on historical data without compromising stability and performance. We leverage a rich set of Key Performance Measurements (KPMs), serving as state for the DRL, to create a comprehensive representation of the RAN, alongside a set of actions that correspond to some control exercised on the RF frontend. We extend ns-O-RAN, an open-source, realistic simulator for 5G and Open RAN built on ns-3, to conduct an extensive data collection campaign. This enables us to train the agents offline with over 300,000 data points and subsequently evaluate the performance of the trained models. Results show that DRL agents improve energy efficiency by adapting to network conditions while minimally impacting the user experience. Additionally, we explore the trade-off between throughput and energy consumption offered by different DRL agent designs.

cs.NI↗

arXiv2024

Table-top nanodiamond interferometer enabling quantum gravity tests

Along the path of unifying quantum theory and general relativity, it has recently been argued that the observation of gravity-induced entanglement could demonstrate the quantum nature of gravity; a few experimental proposals have been advanced, but extreme technological requirements make their implementation still far. Here we propose a table-top interferometer that, by relying on quantum superpositions of steady nanodiamonds, enables less demanding quantum gravity tests requiring short-range time-varying magnetic fields (much easier to implement) and allowing to recycle the exploited quantum probes (granting shorter acquisition times and increased precision).

quant-ph↗

arXiv2023

ns-O-RAN: Simulating O-RAN 5G Systems in ns-3

O-RAN is radically shifting how cellular networks are designed, deployed and optimized through network programmability, disaggregation, and virtualization. Specifically, RAN Intelligent Controllers (RICs) can orchestrate and optimize the Radio Access Network (RAN) operations, allowing fine-grained control over the network. RICs provide new approaches and solutions for classical use cases such as on-demand traffic steering, anomaly detection, and Quality of Service (QoS) management, with an optimization that can target single User Equipments (UEs), slices, cells, or entire base stations. While this comes with the potential to enable intelligent, programmable RANs, there are still significant challenges to be faced, primarily related to data collection at scale, development and testing of custom control logic for the RICs, and availability of Open RAN simulation and experimental tools for the research and development communities. To address this, we introduce ns-O-RAN, a software integration between a real-world near-real-time RIC and an ns-3 simulated RAN which provides a platform for researchers and telco operators to build, test and integrate xApps. ns-O-RAN extends a popular Open RAN experimental framework (OpenRAN Gym) with simulation capabilities that enable the generation of realistic datasets without the need for experimental infrastructure. We implement it as a new open-source ns-3 module that uses the E2 interface to connect different simulated 5G base stations with the RIC, enabling the exchange of E2 messages and RAN KPMs to be consumed by standard xApps. Furthermore, we test ns-O-RAN with the OSC and OpenRAN Gym RICs, simplifying the onboarding from a test environment to production with real telecom hardware controlled without major reconfigurations required. ns-O-RAN is open source and publicly available, together with quick-start tutorials and documentation.

cs.NI↗

arXiv2022

Autonomous Driving From the Sky: Design and End-to-End Performance Evaluation

For autonomous vehicles to operate without human intervention, information sharing from local sensors plays a fundamental role. This can be challenging to handle with bandwidth-constrained communication systems, which calls for the adoption of new wireless technologies, like in the mmwave bands, to solve capacity issues. Another approach is to exploit uav, able to provide human users and their cars with an aerial bird's-eye view of the scene otherwise unavailable, thus offering broader and more centralized observations. In this article we combine both aspects and design a novel framework in which uav, operating at mmwave, broadcast sensory information to the ground as a means to extend the (local) perception range of vehicles. To do so, we conduct a full-stack end-to-end simulation campaign with ns-3 considering real UAV data from the Stanford Drone Dataset, and study four scenarios representing different uav-to-ground communication strategies. Our results focus on the trade-off between centralized data processing in the sky vs. distributed local processing on the ground, with considerations related to the throughput, latency and reliability of the communication process.

cs.NI↗