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Dario Tagliaferri

Publications and source records attributed to Dario Tagliaferri.

At least 37 records · Page 2Linked to original sources

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.

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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.

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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.

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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 Cramér-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.

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Wireless Communications with Space-Time Modulated Metasurfaces

Space-time modulated metasurfaces (STMMs) are a newly investigated technology for next 6G generation wireless communication networks. An STMM augments the spatial phase function with a time-varying one across the elements, 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.

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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 Cramér-Rao bound.

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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 Cramér-Rao bound analysis in both narrowband and spatially wideband operating conditions.

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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.

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Wavefield Networked Sensing: Principles, Algorithms and Applications

Networked sensing refers to the capability of properly orchestrating multiple sensing terminals to enhance specific figures of merit, e.g., positioning accuracy or imaging resolution. Regarding radio-based sensing, it is essential to understand \textit{when} and \textit{how} sensing terminals should be orchestrated, namely the best cooperation that trades between performance and cost (e.g., energy consumption, communication overhead, and complexity). This paper addresses networked sensing from a physics-driven perspective, aiming to provide a general theoretical benchmark to evaluate its \textit{imaging} performance bounds and to guide the sensing orchestration 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 \textit{wavefield networked sensing} is a framework in which multiple sensing terminals are orchestrated during the acquisition process to maximize the imaging quality (resolution and grating lobes suppression) by pursuing the deceptively simple \textit{wavenumber tessellation principle}. We discuss all the cooperation possibilities between sensing terminals and possible killer applications. Remarkably, we show 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.

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Wideband Effects on Near-Field Pose Estimation of Target-Lodged RIS

Reconfigurable intelligent surfaces (RISs) have been recently considered in sensing context to either improve localization performance or to extend the coverage in non-line-of-sight scenarios. While most of the literature considers RISs as statically placed in the environment, the usage of target-lodged RISs is relatively new and could be of interest where the target's reflectivity can/must be increased to improve its detection or parameters' estimation. This letter derives the Cramér-Rao bound (CRB) on the estimation of position and orientation (\textit{pose}) of a target-mounted RIS, in generic conditions: near-field, bistatic and wideband operation (i.e., when the wavefront across the RIS is curved and the employed sensing bandwidth is large enough to obtain a frequency-dependent RIS behavior). In particular, we focus on the wideband effect, that implies a \textit{pose-dependent filtering} on the impinging signal decreasing or increasing the CRB depending on the RIS size and the employed signal bandwidth.

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Target-to-User Association in ISAC Systems With Vehicle-Lodged RIS

Target-to-user (T2U) association is a prerequisite to fully exploit the potential of the sensing function in communication-centric integrated sensing and communication (ISAC) systems, e.g., for beam and blockage management. This letter proposes to purposely mount a RIS on the roof of the vehicular user equipment (VUE), which can serve as an intentional back-reflector towards the base station. By controlling the reflection pattern over time, it is possible to transmit information to the sensing system, i.e., back-reflection as bit 1, no back-reflection as bit 0. The VUEs are configured to back-reflect a Hadamard code sequence, which enables T2U association. The numerical results confirm the validity of our proposal.

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Integrated Sensing and Communication System via Dual-Domain Waveform Superposition

Integrated Sensing and Communication (ISAC) systems are recognised 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 to tune 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.

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Network-Controlled Repeaters vs. Reconfigurable Intelligent Surfaces for 6G mmW Coverage Extension

Network-controlled repeaters (NCR) and reconfigurable intelligent surfaces (RIS) are being considered by the third generation partnership project (3GPP) as valid candidates for range extension in millimeter-wave (mmW, 30-300 GHz frequency) 5G and 6G networks, to counteract large path and penetration losses. Nowadays, there is no definite answer on which of the two technologies is better for coverage extension, since existing comparative studies focused either on communication-related metrics only or on very large-scale analysis, without physical layer details or environmental considerations. This paper aims at comparing NCR and RIS solutions in terms of coverage in an urban scenario. Numerical results suggest a possible usage of both technologies, showing that preferring RIS or NCR depends not only on their physical capabilities and cost (e.g., the number of antenna elements, the angular separation between panels for NCR, etc.) but also on environment geometry.

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Advanced Tri-Sectoral Multi-User Millimeter-Wave Smart Repeater

Smart Repeaters (SR) can potentially enhance the coverage in Millimeter-wave (mmWave) wireless communications. However, the angular coverage of the existing two-panel SR is too limited to make the SR a truly cost-effective mmWave range extender. This paper proposes the usage of a tri-sectoral Advanced SR (ASR) to extend the angular coverage with respect to conventional SR. We propose a multi-user precoder optimization for ASR in a downlink multi-carrier communication system to maximize the number of served User Equipments (UEs) while guaranteeing constraints on per-UE rate and time-frequency resources. Numerical results show the benefits of the ASR against conventional SR in terms of both cumulative spectral efficiency and number of served UEs (both improved by an average factor 2), varying the system parameters.

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IMT to Satellite Stochastic Interference Modeling and Coexistence Analysis of Upper 6 GHz Band Service

The surging capacity demands of 5G networks and the limited coverage distance of high frequencies like millimeter wave (mmW) and sub-terahertz (THz) bands have led to consider the upper 6GHz (U6G) spectrum for radio access. However, due to the presence of the existing satellite (SAT) services in these bands, it is crucial to evaluate the impact of the interference of terrestrial U6G stations to SAT systems. A comprehensive study on the aggregated U6G-to-SAT interference is still missing in the literature. In this paper, we propose a stochastic model of interference (SMI) to evaluate the U6G-to-SAT interference, including the statistical characterization of array gain and clutter loss and considering different interference modes. Furthermore, we propose an approximate geometrical-based stochastic model of interference (GSMI) as an alternative method to SMI when the clutter-loss distribution is unavailable. Our results indicate that given the typical international mobile telecommunication (IMT) parameters, the aggregated interference power is well below the relevant protection criterion, and we prove numerically that the GSMI method overestimates the aggregated interference power with only 2dB compared to the SMI method.

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Conformal Metasurfaces for Recovering Dynamic Blockage in Vehicular Systems

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 vehicles' blockage and the average end-to-end SNR is enhanced up to 25 dB.

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Localizing the Vehicle's Antenna: an Open Problem in 6G Network Sensing

Millimeter Waves (mmW) and sub-THz frequencies are the candidate bands for the upcoming Sixth Generation (6G) of communication systems. The use of collimated beams at mmW/sub-THz to compensate for the increased path and penetration loss arises the need for a seamless Beam Management (BM), especially for high mobility scenarios such as the Vehicle-to-Infrastructure (V2I) one. Recent research advances in Integrated Sensing and Communication (ISAC) indicate that equipping the network infrastructure, e.g., the Base Station (BS), with either a stand-alone radar or sensing capabilities using optimized waveforms, represents the killer technology to facilitate the BM. However, radio sensing should accurately localize the Vehicular Equipment (VE)'s antenna, which is not guaranteed in general. Differently, employing side information from VE's onboard positioning sensors might overcome this limitation at the price of an increased control signaling between VE and BS. This paper provides a pragmatic comparison between radar-assisted and position-assisted BM for mmW V2I systems in a typical urban scenario in terms of BM training time and beamforming gain loss due to a wrong BM decision. Simulation results, supported by experimental evidence, show that the point target approximation of a traveling VE does not hold in practical V2I scenarios with radar-equipped BS. Therefore, the true antenna position has a residual uncertainty that is independent of radar's resolution and implies 50\,\% more BM training time on average. Moreover, there is not a winning technology for BM between BS-mounted radar and VE's onboard positioning systems. They provide complementary performance, depending on position, although outperforming blind BM techniques compared to conventional blind methods. Thus, we propose to optimally combine radar and positioning information in a multi-technology integrated BM solution.

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Motion Estimation and Compensation in Automotive MIMO SAR

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 a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.

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