Sensing in non-line-of-sight (NLOS) is a well-known issue that limits the range of radar-like sensors. Existing approaches rely on either metallic mirrors, that only work under specular reflection, or dynamically-reconfigurable metasurfaces that steer the signal to cover a desired area in NLOS, with the drawback of cost and control signaling. This paper proposes a novel vehicular sensing method, here referred as Stroboscopic Sensing, where a moving source images a desired region of interest (ROI) in NLOS leveraging on source beam sweeping over a sufficiently large portion of a reflection plane, passively pre-configured as a periodic angular deflecting function to illuminate the ROI. Consequently, the source is able to cover the ROI and enhance the spatial resolution of the image, thanks to multiple diverse observation angles of ROI. In the context of vehicular sensing systems, our method allows a moving vehicle to perceive its surroundings beyond obstacles, improving situational awareness. Remarkably, the proposed method achieves near-field imaging with a sequence of far-field acquisitions, thus limiting the implementation complexity. We detail the system design criteria and trade-offs, demonstrating the remarkable benefits of such sensing method, where a possibly moving source can observe a ROI through multiple points of view as if it were static.

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.

Smart Radio Environment (SRE) is a central paradigm in 6 G and beyond, where integrating Smart Radio Environment (SRE) components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a comprehensive planning framework utilizing realistic urban scenarios and channel models to analyze diverse SRE components, including Reconfigurable Intelligent Surface (RIS), Network-Controlled Repeater (NCR), and advanced technologies like Simultaneous Transmitting and Reflecting RIS (STAR-RIS) and Trisectoral NCR (3SNCR). We propose two optimization strategies—Full Coverage Minimum Cost (FCMC) and Maximum Budget-Constrained Coverage (MBCC)—that address key cost and coverage objectives by considering both physical characteristics and scalable costs of each component, influenced by factors such as NCR amplification gain and RIS dimensions. Extensive numerical results demonstrate the significant impact of these models in enhancing network planning efficiency for high-density urban environments.

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.

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.

Space-time modulated metasurfaces (STMMs) are a newly investigated technology for the next 6G generation wireless communication networks. An STMM augments the spatial phase function with a time-varying one across the meta-atoms, allowing for the conveyance of information that possibly modulates the impinging signal. Hence, STMM represents an evolution of reconfigurable intelligent surfaces (RIS), which only design the spatial phase pattern. STMMs convey signals without a relevant increase in the energy budget, which is convenient for applications where energy is a strong constraint. This paper proposes a mathematical model for STMM-based wireless communication, that creates the basics for two potential STMM architectures. One has excellent design flexibility, whereas the other is more cost-effective. The model describes STMM’s distinguishing features, such as space-time coupling, and their impact on system performance. The proposed STMM model addresses the design criteria of a full-duplex system architecture, in which the temporal signal originating at the STMM generates a modulation overlapped with the incident one. The presented numerical results demonstrate the efficacy of the proposed model and its potential to revolutionize wireless communication.

Coherent multistatic radio imaging represents a pivotal opportunity for forthcoming wireless networks, which involves distributed nodes cooperating to achieve accurate sensing resolution and robustness. This paper delves into cooperative coherent imaging for vehicular radar networks. Herein, multiple radar-equipped vehicles cooperate to improve collective sensing capabilities and address the fundamental issue of distinguishing weak targets in close proximity to strong ones, a critical challenge for vulnerable road users’ protection. We prove the significant benefits of cooperative coherent imaging in the considered automotive scenario in terms of both probability of correct detection, evaluated considering several system parameters, as well as resolution capabilities, showcased by a dedicated experimental campaign wherein the collaboration between two vehicles enables the detection of the legs of a pedestrian close to a parked car. Moreover, as coherent processing of several sensors’ data requires very tight accuracy on clock synchronization and sensor’s positioning—referred to as phase synchronization—(such that to predict sensor-target distances up to a fraction of the carrier wavelength), we present a general three-step cooperative multistatic phase synchronization procedure, detailing the required information exchange among vehicles in the specific automotive radar context and assessing its feasibility and performance by hybrid Cramér-Rao bound.

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.

Electromagnetic skins (EMSs) have been recently considered as a booster for wireless sensing, but their usage on mobile targets is relatively novel and could be of interest when the target reflectivity can/must be increased to improve its detection or the estimation of parameters. In particular, when illuminated by a wide-bandwidth signal (e.g., from a radar operating at millimeter waves), vehicles behave like extended targets, since multiple parts of the vehicle’s body effectively contribute to the back-scattering. Moreover, in some cases perspective deformations challenge the correct localization of the vehicle. To address these issues, we propose lodging EMSs on vehicles’ roof to act as high-reflectivity planar retro-reflectors toward the sensing terminal. The advantage is twofold: 1) by introducing a compact high-reflectivity structure on the target, we make vehicles behave like point targets, avoiding perspective deformations and related ranging biases and 2) we increase the reflectivity of the vehicle, improving localization performance. We detail the EMS design from the system-level to the full-wave-level considering both reconfigurable intelligent surfaces (RIS) and cost-effective static passive electromagnetic skins (SP-EMSs). Localization performance of the EMS-aided sensing system is also assessed by Cramér-Rao bound analysis in both narrowband and spatial wideband operating conditions.

Space-time modulated metasurfaces (STMMs) are a recently proposed generalization of reconfigurable intelligent surfaces, which include a proper time-varying phase at the metasurface elements, enabling higher flexibility and control of the reflected signals. The spatial component can be designed to control the direction of reflection, while the temporal one can be adjusted to change the frequency of the reflected signal or to convey information. However, the coupling between the spatial and temporal phases at the STMM can adversely affect its performance. Therefore, this paper analyzes the system parameters that affect the space-time coupling. Furthermore, two methods for space-time decoupling are investigated. Numerical results highlight the effectiveness of the proposed decoupling methods and reveal that the space-time phase coupling increases with the bandwidth of the temporal phase, the size of the STMM, and with grazing angles of incidence onto the STMM.

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.

High precision navigation in urban environment is a fundamental requirement for modern transportation systems. Global Navigation Satellite System (GNSS) and Inertial Measurement Units (IMUs) are the typical sensors on commercial vehicles. In urban environments, however, shadowing and multi-path effects on satellite signals can severely degrade the localization performance of GNSS receivers. Navigation with IMUs, on the other side, requires a precise GNSS signal to correct the typical biases of low-cost IMUs. In order to mitigate these effects, we propose a processing scheme that leverages on recurrent patterns of repeated passages of vehicles in a given region to make the positioning system much more robust. An edge cloud is used to store past trajectory data to form the a-priori dynamical state within any pre-defined area. The estimated a-priori is passed to the incoming vehicles and is combined with their internal state to reduce the GNSS dependency. An experimental campaign validates the proposed scheme, and results are favourably compared with standard Kalman filter navigation.

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle’s ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-calledautofocusalgorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocusrequirementsin typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets’ localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.

In future 6G millimeter-wave/sub-THz vehicle-to-everything communication systems, vehicles are expected to be equipped with massive antenna arrays to compensate for the severe path loss. However, vehicle-to-vehicle (V2V) direct links are prone to blockage by surrounding vehicles. Emerging metasurface technologies enable the control of the electromagnetic wave reflection towards the desired direction to boost communication performance. Reconfigurable intelligent surfaces (RIS), and the pre-configured counterpart intelligent reflecting surfaces (IRS), are promising low-cost relaying systems for 6G. This paper proposes the usage of conformal metasurfaces (C-RIS or C-IRS) deployed on vehicles’ body to mitigate the blockage impact by creating artificial reflections, compensating for the non-flat shape of the vehicle’s body with proper phase patterns. We analytically derive the phase pattern for a cylindrical C-RIS/C-IRS, accounting for the shape of the car body, which generalizes the conventional planar ones. We propose a novel design for optimally pre-configured C-IRS proving its benefits in a V2V highway scenario. The traffic simulation results show a reduction of blockage probability by 20% for C-IRS and 70% for C-RIS as a benchmark, as well as a remarkable improvement in average signal-to-noise ratio by 10–20 dB for C-IRS and 30–40 dB for C-RIS.

Intelligent reflecting surfaces (IRS) will represent a key technology in the upcoming sixth-generation (6G) communication networks to extend the network coverage and overcome link blockage. Research on IRS is expected to take a giant leap in the coming years to address the current technological limitations, mainly regarding the IRS re-configuration in highly dynamic scenarios, such as vehicle-to-vehicle (V2V) ones. This paper proposes a fully passive and low-cost solution based on pre-configured IRS to be lodged on the vehicle's body, which does not require any signaling for re-configuration. However, conventional IRS are planar array and cannot fit with most vehicles' silhouettes. Hence, the proposed design targets conformal surfaces (C-IRS) with an arbitrary shape. In particular, this paper reveals the first experimental findings on the realization of C-IRS operating at 26 GHz and measurements in anechoic chamber, which validate the analytical derivations. To demonstrate the benefits of the proposed solution, numerical simulations in a V2V scenario show that, when the percentage of vehicles equipping a C-IRS increases, the communication becomes more robust to vehicle blockage and the average end-to-end SNR is enhanced up to 25 dB.

Vehicle-to-Everything (V2X) communications are revolutionizing the connectivity of transportation systems supporting safe and efficient road mobility. To meet the growing bandwidth eagerness of V2X services, millimeter-wave (e.g., 5G new radio over spectrum 26.50 - 48.20 GHz) and sub-THz (e.g., 120 GHz) frequencies are being investigated for the large available spectrum. Communication at these frequencies requires beam-type connectivity as a solution for the severe path loss attenuation. However, beams can be blocked, with negative consequences for communication reliability. Blockage prediction is necessary and challenging when the blocker is dynamic in high mobility scenarios such as Vehicle-to-Vehicle (V2V). This paper presents an analytical model to derive the unconditional probability of blockage in a highway multi-lane scenario. The proposed model accounts for the traffic density, the 3D dimensions of the vehicles, and the position of the antennas. Moreover, by setting the communication parameters and a target quality of service, it is possible to predict the signal-to-noise ratio distribution and the service probability, which can be used for resource scheduling. Exhaustive numerical results confirm the validity of the proposed model.

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (UML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less).

Automated driving requires a huge number of on-board sensors to provide advanced functionalities, from parking assistance to emergency braking and environment mapping for target recognition/classification. While low-cost automotive-legacy radars are mostly used for target detection due to their limited angular resolution, vehicular Synthetic Aperture Radar (SAR) is emerging as a promising imaging solution, provided that the motion is known with high accuracy. This paper assesses the benefits of a navigation-augmented SAR system exploiting multiple on-board sensors, e.g., Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), odometers and steering angle sensors. The results confirm the potential of the proposed multi-sensor-aided SAR system to obtain centimeter-level accurate images of the driving scenario.

This paper focuses on advanced pointing strategies enabling high data-rate directional vehicular communications. New emerging technologies aim to meet the challenging performance requirements of enhanced Vehicle-to-Everything (eV2X) applications by using highly collimated beams, which must rely on a very precise beam alignment. In this work, Free-Space Optics (FSO) is considered, and a system architecture is introduced together with algorithms for an accurate alignment of laser beam. The presented architecture exploits on-board sensor data sharing among vehicles to predict the pointing directions for FSO, thus counteracting the detrimental effect of motion, vibrations and tilting of vehicles. A solution is proposed for an accurate prediction of the FSO pointing direction, based on the sharing of vehicle kinematic data over a parallel wireless control link, augmented by prior information extracted from digital maps of the driving environment. Simulation results point out the challenges of a FSO V2V communication and highlight the feasibility of the proposed solution, considering both state of the art technology and future perspective hardware.