arXiv subjects

Michele Zorzi

Publications and source records attributed to Michele Zorzi.

At least 19 recordsLinked to original sources

arXiv2026

Sensing Fast and Distant Targets in Overspread Channels with Zak-OTFS and Modular Arithmetic

Delay-Doppler (DD) modulations, such as Zak-transform-based orthogonal time frequency space (Zak-OTFS) modulation, represent the channel as a sparse set of delay and Doppler shifts, which makes them well suited for radar sensing. Sensing with a single DD frame, however, is unambiguous only when the channel spread factor, i.e., the product of the delay and Doppler spreads, is below one. Long-range mobility scenarios, including drones, airplanes, low Earth orbit (LEO) satellites, and underwater acoustic links, typically violate this crystallization condition, so that a single frame aliases the target delays, Doppler shifts, or both. Existing methods add a time-frequency pilot to a conventional DD frame and resolve the aliasing in only one dimension. We propose a frame design that combines multiple DD bases with different delay and Doppler periods, which together cover an overspread channel, on the same bandwidth and frame size. For two frames, we show that modular arithmetic on the wrapped peak positions uniquely recovers each target by pairing the peaks through their common projection and their gains, and we derive the condition under which this pairing is ambiguous. We implement the design with two superimposed Zak-OTFS frames and propose two receivers: dual-frame sensing (DS), which performs the joint detection directly, and DS with cross-pilot cancellation (DS-XC), which additionally cancels the cross-pilot interference, re-estimates the gains by joint least squares, and removes ghost targets. Both receivers keep a detection rate close to one as the spread factor grows, and DS-XC also achieves the lowest false-alarm rate and channel normalized mean squared error (NMSE). In a 100 km drone scenario, DS-XC detects 98% of the targets, compared with 81% for the best baseline, with a complexity that grows near-linearly with the time-bandwidth product for a fixed number of targets.

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arXiv2026

Sea Trial Validation of the ROS-DESERT Middleware with Autonomous Underwater Vehicles

This paper presents a modular software architecture that enables environmental-aware coordination of heterogeneous Autonomous Underwater Vehicles (AUVs) to improve underwater acoustic connectivity. The architecture combines a Robot Operating System 2 application layer with the DESERT Underwater communication framework through the rmw_desert middleware, and integrates a Robot Operating System 1 bridge to ensure interoperability with legacy vehicle front-seat controllers. This design enables fine-grained, cross-layer configurability of the communication stack and supports onboard processing of environmental measurements to inform adaptive communication behaviors. As a representative use case, this architecture is used to implement a lightweight depth-optimization strategy that exploits environmental awareness and AUV mobility to improve acoustic link performance. The complete software stack is validated through sea trials conducted off the Gulf of La Spezia in littoral water with an average depth of approximately 100m using a deployment involving three AUVs with distinct operational roles. Experimental results indicate that depth-adaptive repositioning yields measurable gains in packet reception at horizontal separation of approximately 1km, while differences are negligible at shorter ranges where the received signal energy remains above demodulation thresholds. Beyond link-level performance the sea trials confirm the feasibility, modularity, and practical deployability of the proposed architecture on existing AUV platforms.

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arXiv2026

End-to-End Simulation of 5G NR Integrated Access and Backhaul Networks for Remote Maritime Connectivity

Millimeter wave (mmWave) 5th generation (5G) networks offer high data rates but face coverage challenges due to severe path loss and blockage. These problems motivate the use of Integrated Access and Backhaul (IAB) as a flexible wireless backhaul solution that extends connectivity to cell boundaries and unfibered areas, including maritime environments. This paper overviews the latest 3GPP specifications for IAB networks in Releases 16 through 18. Then, it presents an ns-3 module for IAB, featuring a complete end-to-end protocol stack, including the backhaul adaptation protocol (BAP) layer, flexible slot and control configurations, and multiplexing schemes based on both time and frequency division. We test the IAB module via extensive system-level simulations in a custom maritime scenario where vessels, equipped with IAB-nodes, can simultaneously act as access points and relays, forming dynamic multi-hop networks that maintain connectivity via wireless backhaul to shore-based stations. We evaluate different topologies and channel conditions, providing insights into the design and deployment of mmWave IAB networks in offshore environments.

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arXiv2026

Toward EU Sovereignty in Space: A Comparative Simulation Study of IRIS 2 and Starlink

The evolution of 6th generation (6G) networks increasingly relies on satellite-based Non-Terrestrial Networks (NTNs) to extend broadband connectivity to remote and unserved regions, and to support public safety. In this paper we compare two representative and conceptually different satellite constellation architectures, namely Starlink and IRIS 2. Starlink is a commercial private Internet constellation by SpaceX, based on dense Low Earth Orbit (LEO) satellites. It is primarily designed to deliver high-capacity broadband services for civil applications, with performance targets comparable to those of terrestrial networks. In contrast, IRIS 2 is a planned public initiative to be deployed by the European Union, based on a multi-layer combination of LEO, Medium Earth Orbit (MEO), and Geo-stationary Earth Orbit (GEO) satellites. It is primarily designed to provide a secure, resilient, and sovereign infrastructure for government and critical communications. After describing the main technical characteristics of Starlink and IRIS 2, we run a comprehensive simulation campaign to evaluate the design tradeoffs between the two. Specifically, we evaluate the per-cell and per-user achievable capacity, the impact of satellite mobility and handover, and identify the capability of each architecture to support global and reliable connectivity. We also provide design suggestions for possible future IRIS 2 deployment extensions.

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arXiv2026

A Joint Reinforcement Learning Scheduling and Compression Framework for Teleoperated Driving

Teleoperated driving (TD) is envisioned as a key application of future sixth generation (6G) networks. In this paradigm, connected vehicles transmit sensor-perception data to a remote (software) driver, which returns driving control commands to enhance traffic efficiency and road safety. This scenario imposes to maintain reliable and low-latency communication between the vehicle and the remote driver. To this aim, a promising solution is Predictive Quality of Service (PQoS), which provides mechanisms to estimate possible Quality of Service (QoS) degradation, and trigger timely network corrective actions accordingly. In particular, Reinforcement Learning (RL) agents can be trained to identify the optimal PQoS configuration. In this paper, we develop and implement two integrated RL agents that jointly determine (i) the optimal compression configuration for TD sensor data to balance the trade-off between transmission efficiency and data quality, and (ii) the optimal scheduling configuration to minimize the end-to-end latency by allocating radio resources according to different priority levels. We prove via full-stack ns-3 simulations that our integrated agents can deliver superior performance than any standalone model that only optimizes either compression or scheduling, especially in constrained or congested networks. While these agents can be deployed using either centralized or decentralized learning, we further propose a new meta-learning agent that dynamically selects the most appropriate strategy between the two based on current network conditions and application requirements.

cs.NI↗

arXiv2026

5G NR Non-Terrestrial Networks: Open Challenges for Full-Stack Protocol Design

As 5th generation (5G) networks continue to evolve, there is a growing interest toward the integration of Terrestrial Networks (TNs) and Non-Terrestrial Networks (NTNs). Specifically, NTNs leverage space/air base stations such as satellites, High Altitude Platforms (HAPs), and Unmanned Aerial Vehicles (UAVs) for expanding wireless coverage to underserved rural/remote areas, supporting emergency communications, and offloading traffic in highly congested urban environments. In this paper we focus on the 3GPP 5G NR-NTN standard in the context of satellite communication networks, and highlight critical challenges that must be addressed for proper full-stack protocol design, with considerations related to the PHY, MAC, and higher layers. We also present simulation results in ns-3 to demonstrate the impact of some of these challenges on the network, as an initial step toward more advanced standardization activities on 3GPP 5G NR-NTN.

cs.NI↗

arXiv2026

5G NR Non-Terrestrial Networks: From Early Results to the Road Ahead

This paper overviews the 3GPP 5G NR-NTN standard, detailing the evolution from Rel. 18 to 19 and innovations for Rel. 20. Using realistic ns-3 simulations validated against 3GPP calibration data, we evaluate various satellite network configurations. The results highlight the potential of NTNs to extend wireless connectivity to remote areas, serve requests during emergency, and alleviate terrestrial network congestion.

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arXiv2026

Experimental Evaluation of a UAV-Mounted LEO Satellite Backhaul for Emergency Connectivity

Reliable connectivity is critical for Public Protection and Disaster Relief operations, especially in rural or compromised environments where terrestrial infrastructure is unavailable. In such scenarios, NTNs, and specifically UAVs, are promising candidates to provide on-demand and rapid connectivity on the ground, serving as aerial base stations. In this paper, we implement a setup in which a rotary-wing UAV, equipped with a Starlink Mini terminal, provides Internet connectivity to an emergency ground user in the absence of cellular coverage via LEO satellites. The UAV functions as a Wi-Fi access point, while backhauling the ground traffic through the Starlink constellation. We evaluate the system via both network simulations in ns-3 and real-world flight experiments in a rural environment, in terms of throughput, latency, coverage, and energy consumption under static and dynamic flight conditions. Our results demonstrate that the system can maintain a stable uplink throughput of approximately 30 Mbps up to approximately 200 meters, and with minimal impact on the UAV battery lifetime. These findings demonstrate the feasibility of deploying commercial LEO satellite terminals on UAVs as a practical solution for emergency connectivity.

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arXiv2025

Network-Aware Control of AGVs in an Industrial Scenario: A Simulation Study Based on ROS 2 and Gazebo

Networked Control System (NCS) is a paradigm where sensors, controllers, and actuators communicate over a shared network. One promising application of NCS is the control of Automated Guided Vehicles (AGVs) in the industrial environment, for example to transport goods efficiently and to autonomously follow predefined paths or routes. In this context, communication and control are tightly correlated, a paradigm referred to as Joint Communication and Control (JCC), since network issues such as delays or errors can lead to significant deviations of the AGVs from the planned trajectory. In this paper, we present a simulation framework based on Gazebo and Robot Operating System 2 (ROS 2) to simulate and visualize, respectively, the complex interaction between the control of AGVs and the underlying communication network. This framework explicitly incorporates communication metrics, such as delay and packet loss, and control metrics, especially the Mean Squared Error (MSE) between the optimal/desired and actual path of the AGV in response to driving commands. Our results shed light into the correlation between the network performance, particularly Packet Reception Ratio (PRR), and accuracy of control.

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arXiv2025

Performance Evaluation of LoRa for IoT Applications in Non-Terrestrial Networks via ns-3

The integration of Internet of Things (IoT) and Non-Terrestrial Networks (NTNs) has emerged as a key paradigm to provide connectivity for sensors and actuators via satellite gateways in remote areas where terrestrial infrastructure is limited or unavailable. Among other Low-Power Wide-Area Network (LPWAN) technologies for IoT, Long Range (LoRa) holds great potential given its long range, energy efficiency, and flexibility. In this paper, we explore the feasibility and performance of LoRa to support large-scale IoT connectivity through Low Earth Orbit (LEO) satellite gateways. To do so, we developed a new ns3-LoRa-NTN simulation module, which integrates and extends the ns3-LoRa and ns3-NTN modules, to enable full-stack end-to-end simulation of satellite communication in LoRa networks. Our results, given in terms of average data rate and Packet Reception Ratio (PRR), confirm that LoRa can effectively support direct communication from the ground to LEO satellites, but network optimization is required to mitigate collision probability when end nodes use the same Spreading Factors (SFs) over long distances.

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arXiv2025

PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence

Predictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. Hence, PQoS is extremely useful for automotive applications such as teleoperated driving, which poses strict constraints in terms of latency and reliability. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimization. In this manuscript, we present PRATA, a new simulation framework to enable PRedictive QoS based on AI for Teleoperated driving Applications. PRATA consists of a modular pipeline that includes (i) an end-to-end protocol stack to simulate the 5G Radio Access Network (RAN), (ii) a tool for generating automotive data, and (iii) an Artificial Intelligence (AI) unit to optimize PQoS decisions. To prove its utility, we use PRATA to design an RL unit, named RAN-AI, to optimize the segmentation level of teleoperated driving data in the event of resource saturation or channel degradation. Hence, we show that the RAN-AI entity efficiently balances the trade-off between QoS and Quality of Experience (QoE) that characterize teleoperated driving applications, almost doubling the system performance compared to baseline approaches. In addition, by varying the learning settings of the RAN-AI entity, we investigate the impact of the state space and the relative cost of acquiring network data that are necessary for the implementation of RL.

cs.NI↗

arXiv2025

Multi-Agent Reinforcement Learning Scheduling to Support Low Latency in Teleoperated Driving

The teleoperated driving (TD) scenario comes with stringent Quality of Service (QoS) communication constraints, especially in terms of end-to-end (E2E) latency and reliability. In this context, Predictive Quality of Service (PQoS), possibly combined with Reinforcement Learning (RL) techniques, is a powerful tool to estimate QoS degradation and react accordingly. For example, an intelligent agent can be trained to select the optimal compression configuration for automotive data, and reduce the file size whenever QoS conditions deteriorate. However, compression may inevitably compromise data quality, with negative implications for the TD application. An alternative strategy involves operating at the Radio Access Network (RAN) level to optimize radio parameters based on current network conditions, while preserving data quality. In this paper, we propose Multi-Agent Reinforcement Learning (MARL) scheduling algorithms, based on Proximal Policy Optimization (PPO), to dynamically and intelligently allocate radio resources to minimize E2E latency in a TD scenario. We evaluate two training paradigms, i.e., decentralized learning with local observations (IPPO) vs. centralized aggregation (MAPPO), in conjunction with two resource allocation strategies, i.e., proportional allocation (PA) and greedy allocation (GA). We prove via ns-3 simulations that MAPPO, combined with GA, achieves the best results in terms of latency, especially as the number of vehicles increases.

cs.NI↗

arXiv2025

Statistical Analysis and End-to-End Performance Evaluation of Traffic Models for Automotive Data

Autonomous driving is a major paradigm shift in transportation, with the potential to enhance safety, optimize traffic congestion, and reduce fuel consumption. Although autonomous vehicles rely on advanced sensors and on-board computing systems to navigate without human control, full awareness of the driving environment also requires a cooperative effort via Vehicle-To-Everything (V2X) communication. Specifically, vehicles send and receive sensor perceptions to/from other vehicles to extend perception beyond their own sensing range. However, transmitting large volumes of data can be challenging for current V2X communication technologies, so data compression represents a crucial solution to reduce the message size and link congestion. In this paper, we present a statistical characterization of automotive data, focusing on LiDAR sensors. Notably, we provide models for the size of both raw and compressed point clouds. The use of statistical traffic models offers several advantages compared to using real data, such as faster simulations, reduced storage requirements, and greater flexibility in the application design. Furthermore, statistical models can be used for understanding traffic patterns and analyzing statistics, which is crucial to design and optimize wireless networks. We validate our statistical models via a Kolmogorov-Smirnoff test implementing a Bootstrap Resampling scheme. Moreover, we show via ns-3 simulations that using statistical models yields comparable results in terms of latency and throughput compared to real data, which also demonstrates the accuracy of the models.

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arXiv2025

Sensing-Based Beamformed Resource Allocation in Standalone Millimeter-Wave Vehicular Networks

In 3GPP New Radio (NR) Vehicle-to-Everything (V2X), the new standard for next-generation vehicular networks, vehicles can autonomously select sidelink resources for data transmission, which permits network operations without cellular coverage. However, standalone resource allocation is uncoordinated, and is complicated by the high mobility of the nodes that may introduce unforeseen channel collisions (e.g., when a transmitting vehicle changes path) or free up resources (e.g., when a vehicle moves outside of the communication area). Moreover, unscheduled resource allocation is prone to the hidden node and exposed node problems, which are particularly critical considering directional transmissions. In this paper, we implement and demonstrate a new channel access scheme for NR V2X in Frequency Range 2 (FR2), i.e., at millimeter wave (mmWave) frequencies, based on directional and beamformed transmissions along with Sidelink Control Information (SCI) to select resources for transmission. We prove via simulation that this approach can reduce the probability of collision for resource allocation, compared to a baseline solution that does not configure SCI transmissions.

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arXiv2025

Performance Evaluation of Satellite-Based Data Offloading on Starlink Constellations

Vehicular Edge Computing (VEC) is a key research area in autonomous driving. As Intelligent Transportation Systems (ITSs) continue to expand, ground vehicles (GVs) face the challenge of handling huge amounts of sensor data to drive safely. Specifically, due to energy and capacity limitations, GVs will need to offload resource-hungry tasks to external (cloud) computing units for faster processing. In 6th generation (6G) wireless systems, the research community is exploring the concept of Non-Terrestrial Networks (NTNs), where satellites can serve as space edge computing nodes to aggregate, store, and process data from GVs. In this paper we propose new data offloading strategies between a cluster of GVs and satellites in the Low Earth Orbits (LEOs), to optimize the trade-off between coverage and end-to-end delay. For the accuracy of the simulations, we consider real data and orbits from the Starlink constellation, one of the most representative and popular examples of commercial satellite deployments for communication. Our results demonstrate that Starlink satellites can support real-time offloading under certain conditions that depend on the onboard computational capacity of the satellites, the frame rate of the sensors, and the number of GVs.

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arXiv2025

Movement- and Traffic-based User Identification in Commercial Virtual Reality Applications: Threats and Opportunities

With the unprecedented diffusion of virtual reality, the number of application scenarios is continuously growing. As commercial and gaming applications become pervasive, the need for the secure and convenient identification of users, often overlooked by the research in immersive media, is becoming more and more pressing. Networked scenarios such as Cloud gaming or cooperative virtual training and teleoperation require both a user-friendly and streamlined experience and user privacy and security. In this work, we investigate the possibility of identifying users from their movement patterns and data traffic traces while playing four commercial games, using a publicly available dataset. If, on the one hand, this paves the way for easy identification and automatic customization of the virtual reality content, it also represents a serious threat to users' privacy due to network analysis-based fingerprinting. Based on this, we analyze the threats and opportunities for virtual reality users' security and privacy.

cs.MM↗

arXiv2024

Performance Evaluation of IoT LoRa Networks on Mars Through ns-3 Simulations

In recent years, there has been a significant surge of interest in Mars exploration, driven by the planet's potential for human settlement and its proximity to Earth. In this paper, we explore the performance of the LoRaWAN technology on Mars, to study whether commercial off-the-shelf IoT products, designed and developed on Earth, can be deployed on the Martian surface. We use the ns-3 simulator to model various environmental conditions, primarily focusing on the Free Space Path Loss (FSPL) and the impact of Martian dust storms. Simulation results are given with respect to Earth, as a function of the distance, packet size, offered traffic, and the impact of Mars' atmospheric perturbations. We show that LoRaWAN can be a viable communication solution on Mars, although the performance is heavily affected by the extreme Martian environment over long distances.

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arXiv2024

Enhanced Time Division Duplexing Slot Allocation and Scheduling in Non-Terrestrial Networks

The integration of non-terrestrial networks (NTNs) and terrestrial networks (TNs) is fundamental for extending connectivity to rural and underserved areas that lack coverage from traditional cellular infrastructure. However, this integration presents several challenges. For instance, TNs mainly operate in Time Division Duplexing (TDD). However, for NTN via satellites, TDD is complicated due to synchronization problems in large cells, and the significant impact of guard periods and long propagation delays. In this paper, we propose a novel slot allocation mechanism to enable TDD in NTN. This approach permits to allocate additional transmissions during the guard period between a downlink slot and the corresponding uplink slot to reduce the overhead, provided that they do not interfere with other concurrent transmissions. Moreover, we propose two scheduling methods to select the users that transmit based on considerations related to the Signal-to-Noise Ratio (SNR) or the propagation delay. Simulations demonstrate that our proposal can increase the network capacity compared to a benchmark scheme that does not schedule transmissions in guard periods.

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