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.

Neural Decoding and Stimulation (ND&S) systems offer a promising alternative to conventional treatments for peripheral nerve injuries by decoding neural signals and delivering targeted stimulation via implantable devices. A primary challenge in ND&S development is the accurate classification of electroneurography (ENG) signals under strict constraints on computational resources, processing time and power constraints. To address this, we propose a Parallel Spiking Neural Network (PSNN) architecture based on a novel spiking neuron model called Parallel Spiking Neurons (PSNs), optimized for ENG classification. The model combines event-driven processing with low computational complexity, making it well-suited for implantable applications. Compared to the state-of-the-art ESCAPE-Net, the PSNN achieves higher test accuracy (87.21%±4.9%) and macro F1-score (84.98%±9.39%), while reducing the number of the required model parameters by 99.97%. These results underscore the effectiveness of PSNNs in achieving high classification accuracy with minimal computational overhead, aligning with the stringent requirements of ND&S systems.

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.

Electromagnetic skins (EMSs) have been considered as boosters for communication performance, from coverage to capacity. The vast majority of the scientific literature considers reconfigurable EMSs, a.k.a. reconfigurable intelligent surfaces, whose phase configuration can be dynamically changed in time as needed. Differently, this work considers low-cost static passive curved EMSs (CEMSs) to be lodged on car doors to alleviate the blockage issue in vehicular networks. CEMSs are pre-configured during the manufacturing process, following the shape of the car door, and they are designed to make car doors behave as anomalous mirrors, and vehicles to operate as opportunistic passive reflectors. This paper delves into the optimization of the CEMS phase profile knowing the coarse a-priori distribution of incident and reflection angles onto the CEMS, as a function of the vehicular traffic pattern. A penalty-based method to optimize both the average spectral efficiency (SE) and average coverage probability is benchmarked against a novel lower complexity and physically intuitivemodular architecture, employing a codebook-based discrete optimization technique. Our numerical results show that whenever the direct path is blocked, by smart preconfiguration of the CEMS, average SE and coverage can be significantly enhanced.

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 methods 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.

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 4× less pilots compared to the current state-of-the-art, marking a substantial advancement in channel estimation efficiency.

Reconfigurable intelligent surfaces (RISs) have been considered recently for target localization. While existing literature typically uses fixed RISs in the environment, mounting RISs on targets is a novel approach that can improve target visibility and positioning. This study derives the Cramér-Rao bound (CRB) for pose estimation (i.e., RIS position and orientation) under a generic wideband and near-field model. The theoretical findings show that a pose-dependent filtering phenomenon occurs, impacting the CRB, which is neglected under narrowband approximation. The extent of this effect varies based on factors such as RIS dimensions and signal bandwidth.

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.

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.