Sensing in NLOS: a Stroboscopic Approach

Sensing in non-line-of-sight (NLOS) is a well-known issue that limits the effective range of radar-like sensors. Existing approaches for NLOS sensing consider the usage of 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 sensing system, that allows a source to image a desired region of interest (ROI) in NLOS, using the combination of a proper beam sweeping (by the source) as well as a passive reflection plane configured as a periodic angular deflecting function (that allows illuminating the ROI). \textit{Stroboscopic sensing} is obtained by sweeping over a sufficiently large portion of the reflection plane, the source covers the ROI \textit{and} enhance the spatial resolution of the image, thanks to multiple diverse observation angles of ROI. Remarkably, the proposed system achieves a 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 a stroboscopic sensing system, where a possibly moving source can observe a ROI through multiple points of view as if it were static.

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.

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.

Multi-View Near-field Imaging in NLOS with Non-Reconfigurable EM Skins

This paper deals with radar imaging in non-line of sight (NLOS) with the aid of non-reconfigurable electromagnetic skins (NR-EMSs). NR-EMSs are passive metasurfaces whose reflection properties are defined during the manufacturing process, and represent a low-cost alternative to reconfigurable intelligent surfaces to implement advanced wave manipulations. We propose and discuss a multi-view near-field radar imaging system where a moving source progressively illuminates different portions of the NR-EMS, whereby each portion (\textit{module}) is purposely phase-configured to focus the impinging radiation over a desired NLOS area of interest. The source, e.g., a radar-equipped vehicle, synthesizes a wide aperture that maps onto the NR-EMS, allowing NLOS imaging with enhanced resolution compared to the standalone radar capabilities. Simulation results show the feasibility and benefits of such an imaging approach and shed light on a possible practical application of metasurfaces for sensing.

Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems

In Integrated Sensing and Communication (ISAC) systems, matching the radar targets with communication user equipments (UEs) is functional to several communication tasks, such as proactive handover and beam prediction. In this paper, we consider a radar-assisted communication system where a base station (BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a double aim: (i) associate vehicular radar targets to vehicular equipments (VEs) in the communication beamspace and (ii) predict the beamforming vector for each VE from radar data. The proposed target-to-user (T2U) association consists of two stages. First, vehicular radar targets are detected from range-angle images, and, for each, a beamforming vector is estimated. Then, the inferred per-target beamforming vectors are matched with the ones utilized at the BS for communication to perform target-to-user (T2U) association. Joint multi-target detection and beam inference is obtained by modifying the you only look once (YOLO) model, which is trained over simulated range-angle radar images. Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace. Moreover, we show that the modified YOLO architecture can effectively perform both beam prediction and radar target detection, with similar performance in mean average precision on the latter over different antenna array sizes.

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 Cram\'er-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.

Cooperative Coherent Multistatic Imaging and Phase Synchronization in Networked Sensing

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 \textit{coherent} processing of several sensors' data requires very tight accuracy on clock synchronization and sensor's positioning -- referred to as \textit{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 Cram\'er-Rao bound.

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.

Reconfigurable and Static EM Skins on Vehicles for Localization

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 \textit{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: \textit{(i)} by introducing a compact high-reflectivity structure on the target, we make vehicles behave like \textit{point targets}, avoiding perspective deformations and related ranging biases and \textit{(ii)} we increase the reflectivity 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 Cram\'er-Rao bound analysis in both narrowband and spatially wideband operating conditions.

Space-Time Phase Coupling in STMM-based Wireless Communications

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.