arXiv subjects

Francesco Linsalata

Publications and source records attributed to Francesco Linsalata.

At least 19 recordsLinked to original sources

arXiv2026

Dual-Orthogonality Waveforms for Integrated Communication and Imaging in Dynamic Multipath Channels

Dual-Orthogonality waveforms are multi-antenna signaling schemes that enforce mutual orthogonality across transmit channels and over a prescribed set of delay shifts. By relaxing strict time orthogonality to the physically admissible propagation region, they preserve full-band operation per transmit antenna while embedding communication data and maintaining stream separability. This makes them attractive for Integrated Sensing and Communications (ISAC), where reliable data transmission, high-resolution sensing, and imaging must coexist under time-varying propagation. In dynamic multipath environments, delay-Doppler dispersion across multiple paths perturbs the transmit subspaces and partially breaks the relaxed orthogonality conditions. This paper analyzes this effect and develops a multipath-aware decoding framework based on structured parameter estimation, effective-subspace reconstruction, and low-complexity linear equalization. Numerical results show communication performance comparable to OFDM-based ISAC and MIMO-OTFS baselines while improving sensing and imaging through full-band per-transmit operation. The proposed approach achieves approximately 30 cm range resolution, more than 15 dB suppression of multipath imaging artifacts with coherent SAR processing, and a favorable sensing-communication trade-off. Over-the-air experiments at 60 GHz validate multi-stream communication, the designed zero-correlation region, and accurate radar ranging. A second campaign in a highly reflective indoor environment further demonstrates multipath-aware stream equalization under strong unsuppressed reflections.

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arXiv2026

Multi-UE Identification and Localization in LAWN via an Autonomous Non-Serving UAV

This paper presents an autonomous sensing framework for identifying and localizing multiple User Equipments (UEs) in Fifth Generation (5G) cellular networks using a non-serving Unmanned Aerial Vehicle (UAV). A complete onboard processing chain is developed to perform synchronization, multi-UE identification, and localization directly from standard 3GPP-compliant uplink Sounding Reference Signals (SRS). Unlike conventional UAV-assisted approaches relying on serving nodes or infrastructure support, the proposed platform operates as a passive sensing UAV, requiring only limited initial coordination with the network and no mission-time control-plane interaction. The approach exploits the structured and periodic nature of SRS transmissions together with a tailored protocol configuration to ensure robust operation under realistic multi-UE interference. The system operates with narrowband SRS (1.4 MHz), reducing UE power consumption and hardware complexity while enabling high multiplexing through cyclic shifts and frequency resources. Reliable synchronization and multi-UE identification are achieved even when multiple UEs share the same resources. The UAV autonomously collects measurements along its trajectory and estimates UE positions using a trajectory-based localization strategy. The proposed framework is validated through extensive simulations and a full-scale experimental campaign, achieving localization errors below 8 m in urban scenarios and below 3 m in rural conditions, outperforming state-of-the-art Angle of Arrival (AoA)- and Time Difference of Arrival (TDoA)-based methods by about 5-6 m. These results demonstrate the feasibility of infrastructure-independent sensing UAVs for Low-Altitude Wireless Networks (LAWN), enabling scalable and rapidly deployable situational awareness in emergency and connectivity-limited environments.

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arXiv2026

Site Geometry and Calibration Uncertainties in Digital Twin-enabled Channel Estimation

Fast ray tracing (RT) has stimulated the Digital Twin (DT) as an emerging technology for environment-aware communications. Since wireless propagation is governed by the interaction between site geometry and electromagnetic (EM) properties of the environment, DT-based approaches can provide site-specific prior information for channel estimation. In this work, we investigate the robustness of DT to aid the channel estimation, where multipath features extracted via RT are used to construct the low-rank (LR) eigenstructure of the channel covariance matrix. This LR structure is used in channel estimation. However, the digital representation of propagation model is inaccurate and thus it affects the LR. We explicitly analyze these model mismatches that arise from user positioning errors, which translate into geometric inconsistencies in the site representation, and EM material calibration errors. We derive a first-order perturbative model that separates geometric perturbations, affecting angles and delays, from EM perturbations, affecting path gains. Based on this perturbed model, we provide a normalized mean-square error (NMSE) analysis that reveals a fundamental difference between geometric and EM perturbations. In particular, we show that LR estimation is inherently robust to EM calibration perturbations, while positioning errors, dominate performance degradation by altering the channel eigenstructure. Numerical results confirm that, in urban, suburban and rural scenarios, positioning errors are the primary limiting factor, whereas EM calibration errors have a comparatively limited impact. Despite these mismatches, DT-empowered estimators provide up to 10dB NMSE improvement, over baseline methods, in the urban low signal-to-noise ratio (SNR) settings, while achieving performance comparable to baseline estimators at high SNR for moderate (< 1 m) positioning errors.

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arXiv2026

VaN3Twin: the Multi-Technology V2X Digital Twin with Ray-Tracing in the Loop

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 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 DSRC and C-V2X technologies operating over 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.

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arXiv2026

Indoor 60 GHz Radio Channel Dataset Enabling Digital Twin Construction

The ambitious performance targets of modern wireless networks, including 6G and Industrial IoT (IIoT) systems, necessitate advanced hardware platforms utilizing millimeter-wave (mmWave) technology. High-frequency signals provide the bandwidth and low latency required for these systems, but rely on beamforming to overcome path loss and exploit channel sparsity. This kind of architecture provides all the specifications needed to build a SLAM (Simultaneous Localization and Mapping) system. This paper presents a dataset based on a validated, high-performance testbed integrating a Xilinx Zynq UltraScale+ RFSoC with a Sivers 60 GHz beamforming front-end. We demonstrate a novel methodology using segment-scrambled, quasi-orthogonal chirp waveforms to perform rapid exhaustive beamspace sampling. The system is integrated with Pynq Linux for real-time control and high-speed waveform upload. We present a high-density spatial dataset consisting of 350 measurement points across a 1.95 m x 3.60 m indoor grid. We exploited the system's ability to scan 63 transmit directions and construct a complete 63x63 beamspace intensity map in 200us. This dataset serves as a benchmark for spatial channel modeling, Digital Twins and Integrated Sensing and Communication (ISAC) research.

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arXiv2026

Predicting Networks Before They Happen: Experimentation on a Real-Time V2X Digital Twin

Emerging safety-critical Vehicle-to-Everything (V2X) applications require networks to proactively adapt to rapid environmental changes rather than merely reacting to them. While Network Digital Twins (NDTs) offer a pathway to such predictive capabilities, existing solutions typically struggle to reconcile high-fidelity physical modeling with strict real-time constraints. This paper presents a novel, end-to-end real-time V2X Digital Twin framework that integrates live mobility tracking with deterministic channel simulation. By coupling the Tokyo Mobility Digital Twin-which provides live sensing and trajectory forecasting-with VaN3Twin-a full-stack simulator with ray tracing-we enable the prediction of network performance before physical events occur. We validate this approach through an experimental proof-of-concept deployed in Tokyo, Japan, featuring connected vehicles operating on 60 GHz links. Our results demonstrate the system's ability to predict Received Signal Strength (RSSI) with a maximum average error of 1.01 dB and reliably forecast Line-of-Sight (LoS) transitions within a maximum average end-to-end system latency of 250 ms, depending on the ray tracing level of detail. Furthermore, we quantify the fundamental trade-offs between digital model fidelity, computational latency, and trajectory prediction horizons, proving that high-fidelity and predictive digital twins are feasible in real-world urban environments.

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arXiv2026

RF Intelligence for Health: Classification of SmartBAN Signals in overcrowded ISM band

Accurate classification of Radio-Frequency (RF) signals is essential for reliable wearable health-monitoring systems, providing awareness of the interference conditions in which medical protocols operate. In the overcrowded 2.4 GHz ISM band, however, identifying low-power transmissions from medical sensors is challenging due to strong co-channel interference and substantial power asymmetry with coexisting technologies. This work introduces the first open source framework for automatic recognition of SmartBAN signals in Body Area Networks (BANs). The framework combines a synthetic dataset of simulated signals with real RF acquisitions obtained through Software-Defined Radios (SDRs), enabling both controlled and realistic evaluation. Deep convolutional neural networks based on ResNet encoders and U-Net decoders with attention mechanisms are trained and assessed across diverse propagation conditions. The proposed approach achieves over 90% accuracy on synthetic datasets and demonstrates consistent performance on real over-the-air spectrograms. By enabling reliable SmartBAN signal recognition in dense spectral environments, this framework supports interferenceaware coexistence strategies and improves the dependability of wearable healthcare systems.

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arXiv2026

Communication Technologies for Intelligent Transportation Systems: From Railways to UAVs and Beyond

This white paper aims to comprehensively analyze and consolidate the state of the art in communication technologies supporting modern and future Information and Communication Technology (ICT). Its primary objective is to establish a common understanding of how communication solutions enable automation, safety, and efficiency across multiple transport domains, including railways, road vehicles, aircraft, and unmanned aerial vehicles. The document seeks to identify key communication requirements and technological enablers necessary for interoperable and reliable ITS operation. It also assesses the limitations of current systems and proposes pathways for integrating emerging technologies such as 5G, Sixth Generation (6G), and Artificial Intelligence (AI)-driven network control. The white paper also intends to support harmonization between different transport modes through a unified framework for communication modeling, testing, and standardization. It highlights the importance of accurate channel modeling and empirical validation to design efficient, robust, and scalable systems. Another objective is to explore the use of reconfigurable intelligent surfaces, integrated sensing and communication, and digital twin concepts within ITS. The document emphasizes the role of spectrum management and standardization efforts in ensuring interoperability among diverse communication systems. Finally, the paper seeks to stimulate collaboration among academia, industry, and standardization bodies to advance the design of resilient and adaptive communication infrastructures for future transportation systems.

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arXiv2025

Chartwin: a Case Study on Channel Charting-aided Localization in Dynamic Digital Network Twins

Wireless communication systems can significantly benefit from the availability of spatially consistent representations of the wireless channel to efficiently perform a wide range of communication tasks. Towards this purpose, channel charting has been introduced as an effective unsupervised learning technique to achieve both locally and globally consistent radio maps. In this letter, we propose Chartwin, a case study on the integration of localization-oriented channel charting with dynamic Digital Network Twins (DNTs). Numerical results showcase the significant performance of semi-supervised channel charting in constructing a spatially consistent chart of the considered extended urban environment. The considered method results in $\approx$ 4.5 m localization error for the static DNT and $\approx$ 6 m in the dynamic DNT, fostering DNT-aided channel charting and localization.

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arXiv2025

High-Fidelity RF Mapping: Assessing Environmental Modeling in 6G Network Digital Twins

The design of accurate Digital Twins (DTs) of electromagnetic environments strictly depends on the fidelity of the underlying environmental modeling. Evaluating the differences among diverse levels of modeling accuracy is key to determine the relevance of the model features towards both efficient and accurate DT simulations. In this paper, we propose two metrics, the Hausdorff ray tracing (HRT) and chamfer ray tracing (CRT) distances, to consistently compare the temporal, angular and power features between two ray tracing simulations performed on 3D scenarios featured by environmental changes. To evaluate the introduced metrics, we considered a high-fidelity digital twin model of an area of Milan, Italy and we enriched it with two different types of environmental changes: (i) the inclusion of parked vehicles meshes, and (ii) the segmentation of the buildings facade faces to separate the windows mesh components from the rest of the building. We performed grid-based and vehicular ray tracing simulations at 28 GHz carrier frequency on the obtained scenarios integrating the NVIDIA Sionna RT ray tracing simulator with the SUMO vehicular traffic simulator. Both the HRT and CRT metrics highlighted the areas of the scenarios where the simulated radio propagation features differ owing to the introduced mesh integrations, while the vehicular ray tracing simulations allowed to uncover the distance patterns arising along realistic vehicular trajectories.

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arXiv2025

AI-empowered Real-Time Line-of-Sight Identification via Network Digital Twins

The identification of Line-of-Sight (LoS) conditions is critical for ensuring reliable high-frequency communication links, which are particularly vulnerable to blockages and rapid channel variations. Network Digital Twins (NDTs) and Ray-Tracing (RT) techniques can significantly automate the large-scale collection and labeling of channel data, tailored to specific wireless environments. This paper examines the quality of Artificial Intelligence (AI) models trained on data generated by Network Digital Twins. We propose and evaluate training strategies for a general-purpose Deep Learning model, demonstrating superior performance compared to the current state-of-the-art. In terms of classification accuracy, our approach outperforms the state-of-the-art Deep Learning model by 5% in very low SNR conditions and by approximately 10% in medium-to-high SNR scenarios. Additionally, the proposed strategies effectively reduce the input size to the Deep Learning model while preserving its performance. The computational cost, measured in floating-point operations per second (FLOPs) during inference, is reduced by 98.55% relative to state-of-the-art solutions, making it ideal for real-time applications.

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arXiv2025

Exploiting Age of Information in Network Digital Twins for AI-driven Real-Time Link Blockage Detection

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.

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arXiv2025

Bayesian EM Digital Twins Channel Estimation

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.

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arXiv2025

Toward Digital Network Twins: Integrating Sionna RT in ns-3 for 6G Multi-RAT Networks Simulations

The increasing complexity of 6G systems demands innovative tools for network management, simulation, and optimization. This work introduces the integration of ns-3 with Sionna RT, establishing the foundation for the first open source full-stack Digital Network Twin (DNT) capable of supporting multi-RAT. By incorporating a deterministic ray tracer for precise and site-specific channel modeling, this framework addresses limitations of traditional stochastic models and enables realistic, dynamic, and multilayered wireless network simulations. Tested in a challenging vehicular urban scenario, the proposed solution demonstrates significant improvements in accurately modeling wireless channels and their cascading effects on higher network layers. With up to 65% observed differences in application-layer performance compared to stochastic models, this work highlights the transformative potential of ray-traced simulations for 6G research, training, and network management.

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arXiv2024

Integrated Communication and Imaging: Design, Analysis, and Performances of COSMIC Waveforms

This paper presents COSMIC (Connectivity-Oriented Sensing Method for Imaging and Communication), an innovative waveform design framework that integrates environmental radio imaging with robust communication capabilities. COSMIC introduces an extended orthogonality condition achieved through algebraic precoding across transmitting antennas, differentiating it from conventional time, frequency, or space multiplexing techniques. By leveraging the constrained imaging field of view relative to the signal duration, COSMIC enables the simultaneous transport of information while preserving high sensing performance. Numerical evaluations reveal that COSMIC significantly outperforms state-of-the-art methods, doubling the imaging Signal-to-Noise Ratio and substantially reducing the Integrated Side Lobe Ratio, thus demonstrating its effectiveness in combining communication and imaging functionalities.

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arXiv2024

Toward Real-Time Digital Twins of EM Environments: Computational Benchmark of Ray Launching Software

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.

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arXiv2024

A Multi-Modal Simulation Framework to Enable Digital Twin-based V2X Communications in Dynamic Environments

Digital Twins (DTs) for physical wireless environments have been recently proposed as accurate virtual representations of the propagation environment that can enable multi-layer decisions at the physical communication equipment. At high-frequency bands, DTs can help to overcome the challenges emerging in high mobility conditions featuring vehicular environments. In this paper, we propose a novel data-driven workflow for the creation of the DT of a Vehicle-to-Everything (V2X) communication scenario and a multi-modal simulation framework for the generation of realistic sensor data and accurate mmWave/sub-THz wireless channels. The proposed method leverages an automotive simulation and testing framework and an accurate ray-tracing channel simulator. Simulations over an urban scenario show the achievable realistic sensor and channel modelling both at the infrastructure and at ego-vehicles. We showcase the proposed framework on the DT-aided blockage handover task for V2X link restoration, leveraging the framework's dynamic channel generation capabilities for realistic vehicular blockage simulation.

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arXiv2024

Advancing O-RAN to Facilitate Intelligence in V2X

Vehicular communications integrated with the Radio Access Network (RAN) are envisioned as a breakthrough application for the 6th generation (6G) cellular systems. However, traditional RANs lack the flexibility to enable sophisticated control mechanisms that are demanded by the strict performance requirements of the vehicle-to-everything (V2X) environment. In contrast, the features of Open RAN (O-RAN) can be exploited to support advanced use cases, as its core paradigms represent 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 required for O-RAN to be capable of facilitating network intelligence in V2X. This article pioneers the integration of the two strategies for seamlessly incorporating V2X control within O-RAN ecosystem. First, an enabling architecture that tightly integrates V2X and O-RAN is proposed and discussed. Then, a set of key V2X challenges is identified, and O-RAN-based solutions are proposed, paired with extensive numerical analysis to support their effectiveness. Results showcase the superior performance of such an approach in terms of raw throughput, network resilience, and control overhead. Finally, these results validate the proposed enabling architecture and confirm the potential of O-RAN in support of V2X communications.

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