arXiv subjects

Silvia Mura

Publications and source records attributed to Silvia Mura.

18 recordsLinked to original sources

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↗

arXiv2025

Integrating Phase-Coherent Multistatic Imaging in Downlink D-MIMO Networks

This paper addresses the challenge of integrating multistatic coherent imaging functionalities in the downlink (DL) of a phase-coherent distributed multiple input multiple output (D-MIMO) communication network. During DL, the D-MIMO access points (APs) jointly precode the transmitted signals to maximize the spectral efficiency (SE) at the users (UEs) locations. However, imaging requires that \textit{(i)} a fraction of the APs work as receivers for sensing and \textit{(ii)} the transmitting APs emit AP-specific and orthogonal signals to illuminate the area to be imaged and allow multistatic operation. In these settings, our contribution is twofold. We propose a novel distributed integrated sensing and communication (D-ISAC) system that superposes a purposely designed AP-specific signal for imaging to the legacy UE-specific communication one, with a tunable trade-off factor. We detail both the imaging waveform design according to the \textit{extended orthogonality condition} and the space-frequency precoder design. Then, we propose an optimized selection strategy for the receiving APs, in order to maximize imaging performance under half-duplex constraints. Extensive numerical results prove the feasibility and benefits of our proposal, materializing the potential of joint multistatic imaging and communications in practical D-MIMO deployments.

eess.SP↗

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.

eess.SP↗

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.

eess.SP↗

arXiv2025

Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance?

We investigate data-aided iterative sensing in bistatic OFDM ISAC systems, focusing on scenarios with co-located sensing and communication receivers. To enhance target detection beyond pilot-only sensing methods, we propose a multi-stage bistatic OFDM receiver, performing iterative sensing and data demodulation to progressively refine ISAC channel and data estimates. Simulation results demonstrate that the proposed data-aided scheme significantly outperforms pilot-only benchmarks, particularly in multi-target scenarios, substantially narrowing the performance gap compared to a genie-aided system with perfect data knowledge. Moreover, the proposed approach considerably expands the bistatic ISAC trade-off region, closely approaching the probability of detection-achievable rate boundary established by its genie-aided counterpart.

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

Joint Optimization of Uplink and Downlink Power in Full-Duplex Integrated Access and Backhaul

We examine the performance of an Integrated Access and Backhaul (IAB) node as a range extender for beyond-5G networks, focusing on the significant challenges of effective power allocation and beamforming strategies, which are vital for maximizing users' spectral efficiency (SE). We present both max-sum SE and max-min fairness power allocation strategies, to assess their effects on system performance. The results underscore the necessity of power optimization, particularly as the number of users served by the IAB node increases, demonstrating how efficient power allocation enhances service quality in high-load scenarios. The results also show that the typical line-of-sight link between the IAB donor and the IAB node has rank one, posing a limitation on the effective SEs that the IAB node can support.

eess.SP↗

arXiv2024

Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy

The sixth generation (6G) of wireless networks introduces integrated sensing and communication (ISAC), a technology in which communication and sensing functionalities are inextricably linked, sharing resources across time, frequency, space, and energy. Despite its popularity in communication, the orthogonal frequency division multiplexing (OFDM) waveform, while advantageous for communication, has limitations in sensing performance within an ISAC network. This paper delves into OFDM waveform design through optimal resource allocation over time, frequency, and energy, maximizing sensing performance while preserving communication quality. During quasi-normal operation, the Base Station (BS) does not utilize all available time-frequency resources, resulting in high sidelobes in the OFDM waveform's ambiguity function, as well as decreased sensing accuracy. To address these latter issues, the paper proposes a novel interpolation technique using matrix completion through the Schatten p quasi-normal approximation, which requires fewer samples than the traditional nuclear norm for effective matrix completion and interpolation. This approach effectively suppresses the sidelobes, enhancing the sensing performance. Numerical simulations confirm that the proposed method outperforms state-of-the-art frameworks, such as standard complaint resource scheduling and interpolation, particularly in scenarios with limited resource occupancy.

eess.SP↗

arXiv2024

Optimizing Curved EM Skins for Opportunistic Relaying in Vehicular Networks

Electromagnetic skins (EMSs) are recognized for enhancing communication performance, spanning from coverage to capacity. While much of the scientific literature focuses on reconfigurable intelligent surfaces that dynamically adjust phase configurations over time, this study takes a different approach by considering low-cost static passive curved EMS (CEMS)s. These are pre-configured during manufacturing to conform to the shape of irregular surfaces, e.g., car doors, effectively transforming them into anomalous mirrors. This design allows vehicles to serve as opportunistic passive relays, mitigating blockage issues in vehicular networks. This paper delves into a novel design method for the phase profile of CEMS based on coarse a-priori distributions of incident and reflection angles onto the surface, influenced by vehicular traffic patterns. A penalty-based method is employed to optimize both the average spectral efficiency (SE) and average coverage probability, and it is compared against a lower-complexity and physically intuitive modular architecture, utilizing a codebook-based discrete optimization technique. Numerical results demonstrate that properly designed CEMS lead to a remarkable improvements in average SE and coverage probability, namely when the direct path is blocked.

eess.SP↗

arXiv2023

Waveform Design for OFDM-based ISAC Systems Under Resource Occupancy Constraint

Integrated Sensing and Communication (ISAC) is one of the key pillars envisioned for 6G wireless systems. ISAC systems combine communication and sensing functionalities over a single waveform, with full resource sharing. In particular, waveform design for legacy Orthogonal Frequency Division Multiplexing (OFDM) systems consists of a suitable time-frequency resource allocation policy balancing between communication and sensing performance. Over time and/or frequency, having unused resources leads to an ambiguity function with high sidelobes that significantly affect the performance of ISAC for OFDM waveforms. This paper proposes an OFDM-based ISAC waveform design that takes into account communication and resource occupancy constraints. The proposed method minimizes the Cramér-Rao Bound (CRB) on delay and Doppler estimation for two closely spaced targets. Moreover, the paper addresses the under-sampling issue by interpolating the estimated sensing channel based on matrix completion via Schatten $p$-norm approximation. Numerical results show that the proposed waveform outperforms the state-of-the-art methods.

eess.SP↗

arXiv2023

Enhanced Channel Estimation in mm-Wave MIMO Systems Leveraging Integrated Communication and Sensing

This paper tackles the challenge of wideband MIMO channel estimation within indoor millimeter-wave scenarios. Our proposed approach exploits the integrated sensing and communication paradigm, where sensing information aids in channel estimation. The key innovation consists of employing both spatial and temporal sensing modes to significantly reduce the number of required training pilots. Moreover, our algorithm addresses and corrects potential mismatches between sensing and communication modes, which can arise from differing sensing and communication propagation paths. Extensive simulations demonstrate that the proposed method requires 4x less pilots compared to the current state-of-the-art, marking a substantial advancement in channel estimation efficiency.

eess.SP↗

arXiv2022

Integrated Sensing and Communication System via Dual-Domain Waveform Superposition

Integrated Sensing and Communication (ISAC) systems are recognised as one of the key ingredients of the sixth generation (6G) network. A challenging topic in ISAC is the design of a single waveform combining both communication and sensing functionalities on the same time-frequency-space resources, allowing to tune the performance of both with partial or full hardware sharing. This paper proposes a dual-domain waveform design approach that superposes onto the frequency-time (FT) domain both the legacy orthogonal frequency division multiplexing (OFDM) signal and a sensing one, purposely designed in the delay-Doppler domain. With a proper power downscaling of the sensing signal w.r.t. OFDM, it is possible to exceed regulatory bandwidth limitations proper of legacy multicarrier systems to increase the sensing performance while leaving communication substantially unaffected. Numerical and experimental results prove the effectiveness of the dual-domain waveform, notwithstanding a power abatement of at least 30 dB of the signal used for sensing compared to the one used for communication. The dual-domain ISAC waveform outperforms both OFDM and orthogonal time-frequency-space (OTFS) in terms of Cramér-Rao bound on delay estimation (up to 20 dB), thanks to its superior resolution, with a negligible penalty on the achievable rate.

cs.IT↗

arXiv2022

Advanced Tri-Sectoral Multi-User Millimeter-Wave Smart Repeater

Smart Repeaters (SR) can potentially enhance the coverage in Millimeter-wave (mmWave) wireless communications. However, the angular coverage of the existing two-panel SR is too limited to make the SR a truly cost-effective mmWave range extender. This paper proposes the usage of a tri-sectoral Advanced SR (ASR) to extend the angular coverage with respect to conventional SR. We propose a multi-user precoder optimization for ASR in a downlink multi-carrier communication system to maximize the number of served User Equipments (UEs) while guaranteeing constraints on per-UE rate and time-frequency resources. Numerical results show the benefits of the ASR against conventional SR in terms of both cumulative spectral efficiency and number of served UEs (both improved by an average factor 2), varying the system parameters.

eess.SP↗

arXiv2022

LoS-Map Construction for Proactive Relay of Opportunity Selection in 6G V2X Systems

Recent advances in Vehicle-to-Everything (V2X) technology and the upcoming sixth-generation (6G) network will dawn a new era for vehicular services with enhanced communication capabilities. Connected and Autonomous Vehicles (CAVs) are expected to deliver a new transportation experience, increasing the safety and efficiency of road networks. The use of millimeter-wave (mmW) frequencies guarantees a huge amount of bandwidth (> 1GHz) and a high data rate (> 10Gbit/s), which are required for CAVs applications. However, high frequency is impaired by severe path loss, and line of sight (LoS) propagation can be easily blocked by static and dynamic obstacles. Several solutions are being investigated, and the most promising one exploits relays. However, traditional relay schemes react to link failure and leverage instantaneous information, which impedes efficient relay selection in highly mobile and complex networks, such as vehicular scenarios. In this context, we propose a novel proactive relaying strategy that exploits the cooperation between CAVs and environment information to predict the dynamic LoS-map, which describes the links' evolution in time. The proactive relaying schemes exploit the dynamic LoS-map to maximize the network connectivity. A novel framework integrating realistic mobility patterns and geometric channel propagation models is proposed to analyze the performance in different scenarios. Numerical simulations suggest that the proactive relaying schemes mitigate beam blockage and maximize the average probability of connecting CAVs with reliable links.

eess.SP↗

arXiv2021

Spatial-Interference Aware Cooperative Resource Allocation for 5G NR Sidelink Communications

Distributed resource allocation (RA) schemes have been introduced in cellular vehicle-to-everything (C-V2X) standard for vehicle-to-vehicle (V2V) sidelink (SL) communications to share the limited spectrum (sub-6GHz) efficiently. However, the recent progress in connected and automated vehicles and mobility services requires a huge amount of available spectrum resources. Therefore, millimeter-wave and sub-THz frequencies are being considered as they offer a large free bandwidth. However, they require beamforming techniques to compensate for the higher path loss attenuation. The current fifth-generation (5G) RA standard for SL communication is inherited from the previous C-V2X standard, which is not suited for beam-based communication since it does not explore the spatial dimension. In this context, we propose a novel RA scheme that addresses the directional component by adding this third spatial dimension to the bandwidth part structure and promotes cooperation between vehicles in resource selection, namely cooperative three-dimensional RA. Numerical results show an average of 10% improvement in packet delivery ratio, an average 50% decrease in collision probability, and a 30% better channel busy ratio compared to the current standard, thus, confirming the validity of the proposed method.

eess.SP↗