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.

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 4 × 8 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.

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.

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.

The increasing demand for reliable Vehicle-to-Everything (V2X) communications and autonomous mobility necessitates sophisticated simulation frameworks and intelligent optimization strategies. This paper presents a Network Digital Twin (NDT) that integrates high-fidelity ray-tracing with real vehicular traffic data to model wireless propagation in dynamic urban environments and derive theoretical localization bounds. By explicitly exploiting multipath reflections, both line-of-sight (LOS) and non-line-of-sight (NLOS), from static and mobile reflectors such as vehicles, the framework supports the design of an optimized precoding scheme for enhanced user equipment (UE) positioning. Numerical results indicate that the proposed NDT-guided method reduces the Position Error Bound (PEB) by approximately 35%, underscoring NDT benefits and the utility of NLOS exploitation for high-accuracy localization in dense urban scenarios.

This paper introduces a new waveform design approach called COSMIC (Connectivity-Oriented Sensing Method for Imaging and Communication). The method enables the creation of radio images of the environment by applying an extended orthogonality condition to the waveforms. Unlike conventional systems that use time, frequency, or space multiplexing, COSMIC achieves orthogonality through algebraic precoding of the signals from all antennas. Additionally, COSMIC takes advantage of the fact that the imaging field of view is much smaller than the length of the transmitted signals, allowing the waveforms to carry communication data without disrupting the sensing function. Simulations show that COSMIC waveforms enable precise environmental imaging while maintaining good communication performance in terms of error rates.

Digital Twin (DT) has emerged as a promising solution for channel estimation. By leveraging high-resolution 3D models of the scenario and ray-tracing simulations, DT could provide valuable site-specific prior knowledge on the channel’s space-time (ST) invariant features of the multipath environment, such as angles of arrival, angles of departure, and propagation delays. However, the real-time characterization of these features imposes computational constraints on ray-tracing simulations, hence limiting the prior knowledge of the multipath environment and corresponding ST features, and degrading estimation accuracy. In this paper, we propose and investigate, for the first time, three distinct DT-empowered low-rank methods for channel estimation, under different degrees of prior knowledge corresponding to limited number of paths provided by DT. Specifically, these methods perform modal projection onto a joint space-time, a spatial, and a temporal subspace. We compare our proposed methods with state-of-the-art techniques, and evaluate their performance in a synthetic scenario. Numerical results show that robustness, when prior knowledge is limited to few paths, is achieved when exploiting only temporal features, while estimation accuracy is attained when joint space-time features are considered.

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 ≈ 4.5 m localization error for the static DNT and ≈ 6 m in the dynamic DNT, fostering DNT-aided channel charting and localization.

The increasing complexity of 6G networks demands advanced tools for network management and simulation. This demo pioneers the integration of ns-3 and NVIDIA Sienna®RT, laying the foundation for the first multi-Radio Access Technologies (RAT) full-stack, open-source Digital Network Twin (DNT). The introduction of a deterministic ray tracer for an accurate channel modeling into ns-3 enables realistic and site-specific simulation which cannot be achieved via stochastic channel models. Tested in a challenging vehicular urban scenario, the proposed framework demonstrates significant improvements in predicting dynamic wireless channels and its impact at higher network layers.

The rapid expansion of connected and autonomous vehicles (CAVs) and the shift towards millimiter-wave (mmWave) frequencies offer unprecedented opportunities to enhance road safety and traffic efficiency. Sidelink communication, enabling direct Vehicle-to-Vehicle (V2V) communications, play a pivotal role in this transformation. As communication technologies transit to higher frequencies, the associated increase in bandwidth comes at the cost of a severe path and penetration loss. In response to these challenges, we investigate a network configuration that deploys beamforming-capable Unmanned Aerial Vehicles (UAVs) as relay nodes. In this work, we present a comprehensive analytical framework with a groundbreaking performance metric, i.e. average access probability, that quantifies user satisfaction, considering factors across different protocol stack layers. Additionally, we introduce two Radio Resources Assignment (RRA) methods tailored for UAVs. These methods consider parameters such as resource availability, vehicle distribution, and latency requirements. Through our analytical approach, we optimize the average access probability by controlling UAV altitude based on traffic density. Our numerical findings validate the proposed model and strategy, which ensures that Quality of Service (QoS) standards are met in the domain of Vehicle-to-Anything (V2X) sidelink communications.

This paper illustrates the potential of an Integrated Sensing and Communication (ISAC) system, operating in the sub-6 GHz frequency range, for Synthetic Aperture Radar (SAR) imaging via an Unmanned Aerial Vehicle (UAV) employed as an aerial base station. The primary aim is to validate the system's ability to generate SAR imagery within the confines of modern communication standards, including considerations like power limits, carrier frequency, bandwidth, and other relevant parameters. The paper presents two methods for processing the signal reflected by the scene. Additionally, we analyze two key performance indicators for their respective fields, the Noise Equivalent Sigma Zero (NESZ) and the Bit Error Rate (BER), using the QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa), demonstrating the system's capability to image buried targets in challenging scenarios. The paper shows simulated Impulse Response Functions (IRF) as possible pulse compression techniques under different assumptions. An experimental campaign is conducted to validate the proposed setup by producing a SAR image of the environment captured using a UAV flying with a Software-Defined Radio (SDR) as a payload.

This paper aims to showcase the potential of an Integrated Communication and Sensing (ISAC) system, operating within the sub-6 GHz frequency range, for Synthetic Aperture Radar (SAR) imaging through an Unmanned Aerial Vehicle (UAV). Our primary goal is to validate the system’s ability to generate SAR imagery under practical constraints dictated by contemporary communication standards, including factors like maximum transmitted power, carrier frequency, occupied bandwidth, Pulse Repetition Frequency, and the number of sub-carriers. The paper provides a detailed description of the Orthogonal Frequency Division Multiplexing (OFDM) signal transmitted by the base station. We compare two methods for range-compressing the signal backscattered by the scene and analyze the Noise Equivalent Sigma Zero (NESZ) under classical line-of-sight conditions and in challenging environments, demonstrating the system’s capability to detect targets under snow. It also showcases simulated Impulse Response Functions (IRF) under various assumptions, as well as real SAR images of the environment obtained using a UAV with a software-defined radar (SDR) integrated as a payload.

As the deployment of fifth generation (5G) mobile wireless networks continues to gain momentum, researchers are already focusing on the challenges and opportunities of the next sixth generation (6G). To meet the ever-increasing demand for higher data rates and support the development of new services, 6G is expected to exploit millimeter wave (mmWave) frequencies. However, the complex propagation characteristics at mmWave require beamforming technology, which introduces significant complexity in the communication system. Herein, we propose a real-time testbed platform to evaluate beamforming and other communication solutions designed for multiple-input multiple-output (MIMO) mmWave-based 6G networks. This platform serves as an enabler for 6G technologies evaluation under realistic propagation conditions, accelerating the development and deployment of robust and efficient 6G networks. To demonstrate the capabilities of our platform, we have implemented a smart relay with real-time beam control and tracking. The platform is able to perform an exhaustive search of 64 reception beams in less than 256 μs. Additionally, the platform can maintain the optimal beam even in mobility scenarios using a gradient-based tracking system that achieves a low overhead of less than 5%, with an update rate of 100 Hz.

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.

The advancement of Connected and Autonomous Vehicle (CAV) technology promises to revolutionize transportation systems, but robust and effective communication among CAVs is needed to ensure safety and efficiency. Vehicle-to-everything (V2X) communication, particularly vehicle-to-vehicle (V2V) communication, offers direct vehicular data exchange without burdening network infrastructure. However, the dynamic nature of vehicular scenarios and the strict application requirements pose critical challenges in the radio resource allocation domain. To address these challenges, this paper proposes an Open RAN (O-RAN)-based solution, leveraging O-RAN’s flexibility and programmability. The proposed solution employs standardized interfaces to collect and analyze traffic data, enabling centralized cross-base station resource allocation. Implemented as an O-RAN xApp, the solution demonstrates superior performance in large-scale vehicular simulations compared to existing radio allocation schemes, showcasing effectiveness in managing diverse traffic profiles and minimizing allocation collisions with negligible overhead. Evaluation against Mode 2 demonstrates the solution’s efficacy with respect to the standard. Overall, the study highlights for the first time O-RAN’s potential in managing radio resources for V2V communication.

Neuropathies are gaining higher relevance in clinical settings, as they risk permanently jeopardizing a person’s life. To support the recovery of patients, the use of fully implanted devices is emerging as one of the most promising solutions. However, these devices, even if becoming an integral part of a fully complex neural nanonetwork system, pose numerous challenges. In this article, we address one of them, which consists of the classification of motor/sensory stimuli. The task is performed by exploring four different types of artificial neural networks (ANNs) to extract various sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification through a comparison of their performance in terms of accuracy, F1-score, and prediction time. The design of the ANNs takes advantage of the modelling of the ENG signal as a multiple-input multiple-output (MIMO) system to describe the measures taken by state-of-the-art implanted nerve interfaces. These are based on the use of multi-contact cuff electrodes to achieve nanoscale spatial discrimination of the nerve activity. The MIMO ENG signal model is another contribution of this paper. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over 90% for signal windows of 100 and 200 ms with a low enough processing time to be effective for pathology recovery.

Integrated sensing and communication (ISAC) systems are recognized 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 tuning 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.

The combination of the effects of Doppler frequency shifts (due to mobility) and phase noise (due to the imperfections of oscillators operating at a high carrier frequency) poses serious challenges to Orthogonal Frequency Division Multiplexing (OFDM) wireless transmissions in terms of channel estimation and phase noise tracking performance and the associated pilot overhead required for that estimation and tracking. In this paper, we use separate sets of Basis Expansion Model (BEM) coefficients for modelling the time variation over intervals of several OFDM symbols of the channel paths and the phase noise process. Based on this model, an efficient solution approximating the maximum-likelihood joint estimation of these BEM coefficients is derived and shown to outperform state-of-the-art phase noise compensation methods.

Integrated sensing and communication (ISAC) is one of the key technologies of future 6G communication networks. Waveform design for 6G ISAC systems shall guarantee a flexible communication and sensing performance trade-off with full time-frequency-space resource sharing and minimal added hardware/complexity. Legacy ISAC schemes based on orthogonal frequency division multiplexing (OFDM) or orthogonal time-frequency-space (OTFS) are currently subject to regulatory bandwidth constraints, limiting the delay/range resolution and requiring advanced processing schemes. This paper proposes to exploit a low-power, wide-bandwidth out-of-band (OOB) sensing signal superposed to the legacy OFDM one to enhance the delay/range resolution compared to standalone OFDM and OTFS ISAC systems. The proper power control of the sensing signal allows for complying with adjacent channel leakage ratio requirements. The analytical findings demonstrate the advantages of the proposed ISAC scheme over existing solutions.

Vehicle-to-vehicle (V2V) resource allocation (RA) schemes have been introduced in the cellular V2V (C-V2V) standard for sidelink (SL) communications to allow for an efficient sharing of the time-frequency resources in sub-6 GHz bands. However, the recent progress in connected and automated vehicles and the introduction of new bandwidth-eager mobility services are driving towards the use of millimeter-wave (mmW) frequencies (24.25-52.6 GHz). A characteristic of propagation at mmW frequencies is the severe path loss attenuation that can be compensated through beamforming. Therefore, its introduction adds a spatial dimension that must be considered in the design of RA schemes. The current fifth-generation (5G) RA standard for SL communication, which is inherited from the previous C-V2V standard, is not designed for directional communication and does not take into account the interference impact. Hence, this paper proposes a novel RA scheme to manage spatial-interference by adding the spatial dimension, i.e. the spatial beam directivity, and cooperation between vehicles for resource selection. The simulation results confirm that the three-dimensional cooperative RA (3D-CRA) has an average improvement of 10% in packet delivery ratio, 50% in collision probability, and 60% in channel busy ratio compared to the standard RA.

The adoption of Unmanned Aerial Vehicles (UAVs) for public safety applications has skyrocketed in the last years. Leveraging on Physical Random Access Channel (PRACH) preambles, in this paper we pioneer a novel localization technique for UAVs equipped with cellular base stations used in emergency scenarios. We exploit the new concept of Orthogonal Time Frequency Space (OTFS) modulation (tolerant to channel Doppler spread caused by UAVs motion) to build a fully standards-compliant OTFS-modulated PRACH transmission and reception scheme able to perform time-of-arrival (ToA) measurements. First, we analyze such novel ToA ranging technique, both analytically and numerically, to accurately and iteratively derive the distance between localized users and the points traversed by the UAV along its trajectory. Then, we determine the optimal UAV speed as a trade-off between the accuracy of the ranging technique and the power needed by the UAV to reach and keep its speed during emergency operations. Finally, we demonstrate that our solution outperforms standard PRACH-based localization techniques in terms of Root Mean Square Error (RMSE) by about 20% in quasi-static conditions and up to 80% in high-mobility conditions.

The Gabor transform interpretation of the Generalized Frequency-Division Multiplexing (GFDM) leads to a modeling of the effect of the multipath channel as a Multiple-Input Multiple-Output (MIMO) system on each sub-carrier. In this paper, such a modeling is used to propose new pre-coding and decoding design approaches. Two different power allocation strategies based on a joint transmitter and receiver linear design are introduced. By exploiting the circularity of the resulting MIMO channel on each sub-carrier, an eigendecomposition can be implemented, once and for all, by computing the Discrete Fourier transform. The first proposed power allocation approach guarantees fairness among sub-carriers, while the second minimizes the error rate at the price of unfairness. The benefits achieved by the two approaches are demonstrated by numerical simulations and by comparison with other GFDM equalization and pre/de-coding schemes that, in contrast to the proposed one, work on a sub-symbol basis.

Generalized Frequency-Division Multiplexing (GFDM) is a candidate modulation for future wireless cellular networks. The main reason stands in the flexibility of its structure that could fulfill ideas and challenges of forthcoming network scenarios. A known issue of GFDM is its worse performance compared to orthogonal frequency-division multiplexing, which is due to the interference among transmitted symbols. A mathematical model of such an interference has been proposed in a recent paper by exploiting the parallelism that exists between GFDM and discrete Gabor transform. The model allows for the design of different types of linear and non-linear equalizers. With the goal of increasing the transmission reliability, in this paper the introduction of channel coding is considered together with an appropriate interleaving. The computation of the Log-Likelihood Ratio (LLR) is described, which allows for soft decoding in the case of maximum likelihood and linear minimum mean squared error detection. The gain in performance achieved with channel coding and time-frequency interleaving is demonstrated by means of Monte Carlo simulations for the standard 64-state rate-1/2 convolutional code. A comparison with an approach for soft decoding proposed in the literature for GFDM is also reported.