arXiv subjects

Maurizio Magarini

Publications and source records attributed to Maurizio Magarini.

At least 19 recordsLinked to original sources

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.

eess.SP↗

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.

eess.SP↗

arXiv2026

Modeling Plant Action Potentials under Photoperiod Stress via Hodgkin-Huxley Dynamics

Plants exhibit dynamic bioelectric properties that facilitate information transfer across tissues. This study investigates action potentials (APs) in Nicotiana tabacum recorded within a custom-designed growth chamber using a biosignal amplifier and environmental sensors. Consistent light- and dark-induced APs were observed during photoperiod transitions under controlled 12-hour artificial illumination cycles. To understand these bioelectric responses, a mathematical model based on the Hodgkin-Huxley framework is used. Electrophysiological measurements from Solanum lycopersicum revealed that under natural light conditions, only light-induced APs are observed, while light- and dark-induced APs coupled dynamics is exclusively elicited during rapid transitions in artificial photoperiods. These distinct phenomena are characterized as Prolonged Oscillatory Climatic Engagement (POCE) and Nimble Environmental Transition Oscillation (NETO), respectively. The model successfully reproduces the key features in both frameworks while maintaining computational efficiency through voltage-independent rate parameters.

physics.bio-ph↗

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.

eess.SP↗

arXiv2026

A Physics-Based Digital Human Twin for Galvanic-Coupling Wearable Communication Links

This paper presents a systematic characterization of wearable galvanic coupling (GC) channels under narrowband and wideband operation. A physics-consistent digital human twin maps anatomical properties, propagation geometry, and electrode-skin interfaces into complex transfer functions directly usable for communication analysis. Attenuation, phase delay, and group delay are evaluated for longitudinal and radial configurations, and dispersion-induced variability is quantified through attenuation ripple and delay standard deviation metrics versus bandwidth. Results confirm electro-quasistatic, weakly dispersive behavior over 10 kHz-1 MHz. Attenuation is primarily geometry-driven, whereas amplitude ripple and delay variability increase with bandwidth, tightening equalization and synchronization constraints. Interface conditioning (gel and foam) significantly improves amplitude and phase stability, while propagation geometry governs link budget and baseline delay. Overall, the framework quantitatively links tissue electromagnetics to waveform distortion, enabling informed trade-offs among bandwidth, interface design, and transceiver complexity in wearable GC systems.

cs.ET↗

arXiv2026

Trade-Offs in FMCW Radar-Based Respiration and Heart Rate Variability

This study presents a comprehensive experimental assessment of a low-cost frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar for non-contact vital sign monitoring, focusing on respiratory rate (RR) and heart rate (HR) estimation. The influence of sensing distance and number of transmitted chirps on measurement accuracy is systematically quantified. Results exhibit a U-shaped error profile with optimal performance near $70~cm$, achieving mean absolute errors of $0.8~bpm$ for RR and $3.2~bpm$ for HR. Accuracy deteriorates at short ($<60~cm$) and long ($>100~cm$) distances due to multipath, near-field, and signal-to-noise effects. Increasing chirp count enhances performance: RR errors converge asymptotically for $\geq96$ chirps, while HR requires at least 96 chirps for stable detection. Variability metrics, including heart and respiratory rate variability, remain less accurate ($>15$--$30\%$ error), indicating limited capability in capturing instantaneous fluctuations. These findings define a fundamental trade-off: the radar ensures robust estimation of average RR and HR but exhibits restricted precision in high-resolution beat-to-beat and breath-to-breath monitoring.

cs.ET↗

arXiv2026

Experimental Characterization of Biological Tissue Dielectric Properties through THz Time-Domain Spectroscopy

Terahertz (THz) radiation provides a non-ionizing, highly sensitive probe of the dielectric properties of biological tissues. In this study, we present a comprehensive experimental characterization of dielectric properties using pork skin tissue, a widely used surrogate for human tissue, as a biological sample. Measurements are conducted employing THz time-domain spectroscopy in the 0.1-11 THz frequency range with photoconductive antennas for both signal generation and detection. Frequency-dependent refractive indices, absorption, and complex permittivity are extracted from transmitted time-domain signals. Our results confirm strong absorption and low transmittance at low THz frequencies due to water content, while highlighting frequency-dependent dispersion and narrowband transmission features at higher frequencies. This work provides one of the first extended-frequency datasets of biological tissue dielectric properties, supporting realistic channel modeling for the design and development of intra-body nanosensor networks in the THz band.

physics.optics↗

arXiv2026

Layered Dielectric Characterization of Human Skin in the Sub-Terahertz and Terahertz Frequency Ranges

Sub-terahertz (sub-THz) and terahertz (THz) radiation offer unique opportunities for non-invasive diagnostics and imaging due to their sensitivity to water content and molecular dynamics in biological tissues. In this work, a comprehensive dielectric model of human skin and its cellular constituents is developed across these frequency ranges. The model combines multi-Debye relaxation theory with effective medium formulations to account for intracellular water dynamics and macromolecular relaxation processes. Key cellular parameters, including water content, protein and lipid fractions, and ionic conductivity, are integrated from experimentally validated sources. The proposed framework enables realistic predictions of frequency-dependent permittivity for different skin layers and cell types, providing a physically interpretable description of sub-THz and THz tissue interactions. This approach establishes a foundation for the design and optimization of next-generation diagnostic and imaging techniques operating in these frequency bands.

cs.ET↗

arXiv2026

Digital Twin-Based Beamforming for Interference Mitigation in AF Relay MIMO Systems

Beamforming in multiple-input multiple-output (MIMO) systems should take interference mitigation into account. However, for beamform design, accurate channel state information (CSI) is needed, which is often difficult to obtain due to channel variability, feedback overhead, or hardware constraints. For example, amplify-and-forward (AF) relays passively forward signals without measurement, precluding full CSI acquisition to and from the relay. To address these issues, this paper introduces a novel prediction-assisted optimization (PAO) framework for beamform design in AF relay-assisted multiuser MIMO systems. The proposed solution in the AF relay aims at maximizing the signal-plus-interference-to-noise ratio (SINR). Unlike other methods, PAO relies solely on received power measurements, making it suitable for scenarios where CSI is unreliable or unavailable. PAO consists of two stages: a supervised-learning-based neural network (NN) that predicts the positions of transmitters using signal observations, and an optimization algorithm, guided by a digital twin (DT), that iteratively refines the beam direction of the relay in a simulated radio environment. As a key contribution, we validate the proposed framework using realistic measurements collected on a custom-built experimental millimeter wave (mmWave) platform, which enables training of the NN model under practical wireless conditions. The estimated information is then used to update the digital twin with knowledge of the surrounding environment, enabling online optimization. Numerical results show the trade-off between localization accuracy and beamforming performance and confirm that PAO maintains robustness even in the presence of localization errors while reducing the need for real-world measurements.

eess.SP↗

arXiv2026

CoBA: Integrated Deep Learning Model for Reliable Low-Altitude UAV Classification in mmWave Radio Networks

Uncrewed Aerial Vehicles (UAVs) are increasingly used in civilian and industrial applications, making secure low-altitude operations crucial. In dense mmWave environments, accurately classifying low-altitude UAVs as either inside authorized or restricted airspaces remains challenging, requiring models that handle complex propagation and signal variability. This paper proposes a deep learning model, referred to as CoBA, which stands for integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention which leverages Fifth Generation (5G) millimeter-wave (mmWave) radio measurements to classify UAV operations in authorized and restricted airspaces at low altitude. The proposed CoBA model integrates convolutional, bidirectional recurrent, and attention layers to capture both spatial and temporal patterns in UAV radio measurements. To validate the model, a dedicated dataset is collected using the 5G mmWave network at TalTech, with controlled low altitude UAV flights in authorized and restricted scenarios. The model is evaluated against conventional ML models and a fingerprinting-based benchmark. Experimental results show that CoBA achieves superior accuracy, significantly outperforming all baseline models and demonstrating its potential for reliable and regulated UAV airspace monitoring.

cs.LG↗

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.

cs.NI↗

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.

eess.SP↗

arXiv2025

Low-Complexity CNN-Based Classification of Electroneurographic Signals

Peripheral nerve interfaces (PNIs) facilitate neural recording and stimulation for treating nerve injuries, but real-time classification of electroneurographic (ENG) signals remains challenging due to constraints on complexity and latency, particularly in implantable devices. This study introduces MobilESCAPE-Net, a lightweight architecture that reduces computational cost while maintaining and slightly improving classification performance. Compared to the state-of-the-art ESCAPE-Net, MobilESCAPE-Net achieves comparable accuracy and F1-score with significantly lower complexity, reducing trainable parameters by 99.9\% and floating point operations per second by 92.47\%, enabling faster inference and real-time processing. Its efficiency makes it well-suited for low-complexity ENG signal classification in resource-constrained environments such as implantable devices.

eess.SP↗

arXiv2025

Semantic Communications via Features Identification

The development of the new generation of wireless technologies (6G) has led to an increased interest in semantic communication. Thanks also to recent developments in artificial intelligence and communication technologies, researchers in this field have defined new communication paradigms that go beyond those of syntactic communication to post-Shannon and semantic communication. However, there is still need to define a clear and practical framework for semantic communication, as well as an effective structure of semantic elements that can be used in it. The aim of this work is to bridge the gap between two post-Shannon communication paradigms, and to define a robust and effective semantic communication strategy that focuses on a dedicated semantic element that can be easily derived from any type of message. Our work will take form as an innovative communication method called identification via semantic features, which aims at exploiting the ambiguities present in semantic messages, allowing for their identification instead of reproducing them bit by bit. Our approach has been tested through numerical simulations using a combination of machine learning and data analysis. The proposed communication method showed promising results, demonstrating a clear and significant gain over traditional syntactic communication paradigms.

cs.IT↗

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.

cs.NI↗

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.

eess.SP↗

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.

eess.SP↗

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.

eess.SP↗