Francesco Linsalata

This paper presents VaN3Twin—the first open source, full-stack Network Digital Twin (NDT) framework for simulating the coexistence of multiple Vehicle-to-Everything (V2X) communication technologies with accurate physical-layer modeling via ray tracing. VaN3Twin extends the ms-van3t simulator by integrating Sionna Ray Tracer (RT) in the loop, enabling high-fidelity representation of wireless propagation, including diverse Line of Sight (LoS) conditions with a focus on LoS blockage due to other vehicles’ meshes, Doppler effect, and site-dependent effects—e.g., scattering and diffraction. Unlike conventional simulation tools, the proposed framework supports realistic coexistence analysis across Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) technologies operating over a shared spectrum. A dedicated interference tracking module captures cross-technology interference at the time-frequency resource block level and enhances Signal to Interference plus Noise Ratio (SINR) estimation by eliminating artifacts such as the bimodal behavior induced by separate LoS/NLoS propagation models. Compared to field measurements, VaN3Twin reduces application-layer disagreement by 50% in rural and over 70% in urban environments with respect to current state of the art simulation tools, demonstrating its value for scalable and accurate digital twin–based V2X coexistence simulation.

This paper addresses the challenge of achieving simultaneous synchronization and localization of all the active terminals within a cellular network from only one Base Station (BS). We propose a novel approach leveraging Digital Network Twins (DNT), which integrates these two critical tasks within a unified framework.We begin by analyzing User Equipment (UE)-to-network time synchronization, both theoretically and through experimental validation using a 5th generation (5G) testbed, identifying it as the primary obstacle to accurate localization. Then, to address this challenge, we introduce a DNT-based framework that leverages high-fidelity ray-tracing simulations on a 3D digital replica of the environment. This enables precise UE-to-network alignment, dynamic environmental mapping, and accurate real-time localization starting from one Next Generation Node Base (gNB). The proposed method integrates Angle Delay Channel Power Matrix (ADCPM) characterization and Time of Flight (ToF) data with the DNT prior knowledge of the environment, eliminating the need for network cooperation or prior on-field channel measurements for precise localization. We first validate the proposed approach through an outdoor measurement campaign and then demonstrate its effectiveness via numerical simulations, compared to existing localization techniques in scenarios where only a single gNB is available. The method achieves on average a positioning accuracy of less than 6m in the static case and 8m in the dynamic scenario, using a ray-tracing granularity that is not excessively fine (4 × 4 m), even under worst-case synchronization and Non-Line of Sight (NLoS) conditions.

The Line-of-Sight (LoS) identification is crucial to ensure reliable high-frequency communication links, especially those vulnerable to blockages. Network Digital Twins and Artificial Intelligence are key technologies enabling blockage detection (LoS identification) for high-frequency wireless systems, e.g., 6>GHz. In this work, we enhance Network Digital Twins by incorporating Age of Information (AoI) metrics, a quantification of status update freshness, enabling reliable real-time blockage detection (LoS identification) in dynamic wireless environments. By integrating raytracing techniques, we automate large-scale collection and labeling of channel data, specifically tailored to the evolving conditions of the environment. The introduced AoI is integrated with the loss function to prioritize more recent information to fine-tune deep learning models in case of performance degradation (model drift). The effectiveness of the proposed solution is demonstrated in realistic urban simulations, highlighting the trade-off between input resolution, computational cost, and model performance. A resolution reduction of 4x8 from an original channel sample size of (32, 1024) along the angle and subcarrier dimension results in a computational speedup of 32 times. The proposed fine-tuning successfully mitigates performance degradation while requiring only 1% of the available data samples, enabling automated and fast mitigation of model drifts.

Vehicular communications integrated with the radio access network (RAN) are envisioned as a breakthrough application for the evolution of cellular networks, including both current fifth generation (5G) and future sixth generation (6G) systems. However, traditional RANs face challenges in achieving the flexibility required to optimize sophisticated control mechanisms and enhance performance metrics, such as latency and reliability, to meet the diverse and demanding requirements of the vehicle-to-everything (V2X) environment. In contrast, the features of Open RAN (O-RAN) support advanced use cases, as its core paradigms provide an ideal framework for orchestrating vehicular communication. Although the high potential stemming from their integration can be easily seen and recognized, the effective combination of the two ecosystems is an open issue. Conceptual and architectural advances are mandatory for O-RAN to be capable of facilitating network intelligence in V2X. In this work, we introduce mobi-O-RAN, a technological proposition pioneering the integration of the two strategies for seamlessly incorporating V2X control within O-RAN’s ecosystem. First, mobi-O-RAN’s architecture is proposed and discussed. Then, a set of key V2X challenges is identified, and mobi-O-RAN-based solutions are proposed, paired with extensive numerical analysis to support their effectiveness. While our study primarily focuses on 5G NR V2X, we also highlight how mobi-O-RAN can serve as a foundation for future 6G vehicular networks, leveraging its programmability and network intelligence capabilities. Results prove the improved performance of mobi-O-RAN in terms of raw throughput, network resilience, and control overhead. Our results validate the proposed enabling architecture and confirm the potential of mobi-O-RAN in support of V2X communications.

This letter proposes a Bayesian channel estimation method that leverages on the a priori information provided by the Electromagnetic Digital Twin’s (EM-DT) representation of the environment. The proposed approach is compared with several conventional techniques in terms of Normalized Mean Square Error (NMSE), spectral efficiency, and number of pilots. Simulations prove more than 10 dB gain in NMSE and a spectral efficiency comparable to that of the ideal channel state information, for different signal-to-noise ratio (SNR) values. Additionally, the Bayesian EM-DT-empowered channel estimation enables a remarkable pilot reduction compared to maximum likelihood methods at low SNR.

Digital Twin has emerged as a promising paradigm for accurately representing wireless communication electromagnetic environments. The resulting virtual representation of reality facilitates comprehensive insights into the propagation environment, empowering multi-layer decision-making processes at the physical communication level. This paper investigates the impact of ray-based model simulation within real-time Digital Twins. A benchmark for ray-based propagation simulations is presented to evaluate computational time, considering two urban scenarios characterized by different mesh complexity, single and multiple wireless link configurations, and simulations with/without diffuse scattering. Exhaustive empirical analyses are performed showing the behavior of different ray-based solutions. By offering standardized simulations and scenarios, this work provides a technical benchmark for practitioners involved in the implementation of real-time Digital Twins and optimization of ray-based propagation models.

This paper explores the potential of an Integrated Sensing and Communication (ISAC) system to enhance search-and-rescue operations. While prior research has explored ISAC capabilities in Unmanned Aerial Vehicles (UAVs), our study focuses on addressing the specific challenges posed by modern communication standards (e.g., power, frequency, and bandwidth limitations) in the context of search-and-rescue missions. The paper details effective methods for processing echoed signals generated by downlink transmissions and evaluates key performance indicators, including Noise Equivalent Sigma Zero (NESZ) and channel capacity. Additionally, we utilize synchronization uplink signals transmitted by User Equipment (UE) to improve target detection and classification of possible victims by fusing SAR imagery with triangulation results from uplink signals. An experimental campaign validates the proposed setup by integrating SAR images of the environment with active localization results, both produced by a UAV equipped with a Software Defined Radio (SDR) payload. Our results demonstrate the system’s capability to detect and localize buried targets in avalanche scenarios, with localization errors ranging from centimeters to 10 m depending on environmental conditions. This successful integration highlights the practical applicability of our approach in challenging search-and-rescue missions.

Direct communication among vehicles, namely vehicle-to-vehicle (V2V), as part of vehicle-to-everything (V2X) system, will enable most of the upcoming Connected and Autonomous Vehicles (CAVs) applications. Nevertheless, the challenging requirements imposed by the vehicular environment necessitate advanced control mechanisms. Traditional Radio Access Networks (RANs) lack the necessary flexibility to address these control needs. The emerging Open RAN (O-RAN) concept offers a promising solution for orchestrating the V2X network. However, the seamless integration of these ecosystems remains an unresolved challenge. This article promotes a novel integration architecture that ensures the V2X control within O-RAN. We propose a novel architecture that establishes a low frequencies O-RAN-based control plane to ensure reliable and efficient multihops connectivity among CAVs at millimeter wave (mmWave). To examine and test the technological feasibility of this integrated architecture, we extend current Network Simulator 3 (ns-3) modules, leading to a simulation framework for experimenting with O-RAN-empowered V2X system. Such an integrated framework is used to show the benefits in a specific V2V challenge: link and relays selection. This case study serves as a practical example of a new O-RAN solution that effectively addresses the challenges of V2X communications. Numerical results showcase the improved reliability up to 60 to distributed and unmanaged solutions, supporting the viability of the proposed architecture