Marouan Mizmizi

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.

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 intuitive modular architecture, employing a codebook-based discrete optimization technique. Our numerical results show that whenever the direct path is blocked, by smart pre-configuration 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 letter proposes a Bayesian channel estimation method that leverages on the a priori information provided by the Electromagnetic Digital Twin’s (EM-DT) representation of the environment. The proposed approach is compared with several conventional techniques in terms of Normalized Mean Square Error (NMSE), spectral efficiency, and number of pilots. Simulations prove more than 10 dB gain in NMSE and a spectral efficiency comparable to that of the ideal channel state information, for different signal-to-noise ratio (SNR) values. Additionally, the Bayesian EM-DT-empowered channel estimation enables a remarkable pilot reduction compared to maximum likelihood methods at low SNR.

Networked sensing refers to the capability of multiple wireless terminals to cooperate with the aim of enhancing specific figures of merit, e.g., positioning accuracy or imaging resolution. Regarding radio-based sensing, it is essential to understand when and how sensing terminals should cooperate, namely the best strategy that trades between performance and cost (e.g., energy consumption, communication overhead, and complexity). This tutorial paper revises networked sensing from a wavefield interaction perspective, aiming to provide a general theoretical benchmark to evaluate its imaging performance bounds and to guide the sensing cooperation accordingly. Diffraction tomography theory (DTT) is the method to quantify the imaging resolution of any radio sensing experiment from inspection of its spectral (or wavenumber) content. In networked sensing, the image formation is based on the back-projection integral, valid for any network topology and physical configuration of the terminals. The wavefield networked sensing is a framework in which multiple sensing terminals cooperate during the acquisition process to maximize the imaging quality (resolution and sidelobes suppression) by pursuing the wavenumber tessellation principle. We discuss all the coherent data fusion possibilities between sensing terminals and possible killer applications. Remarkably, we show the possibility that the proposed method allows obtaining high-quality images of the environment in limited bandwidth conditions, leveraging the coherent combination of multiple multi-static low-resolution images.

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: (i) 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 (ii) we increase the reflectivity of the vehicle, improving localization performance. We detail the EMS design from the system-level to the full-wavelevel 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.

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-multipleoutput (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

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.