arXiv subjects

Dario Tagliaferri

Publications and source records attributed to Dario Tagliaferri.

47 records · Page 3Linked to original sources

arXiv2022

Conformal Metasurfaces: a Novel Solution for Vehicular Communications

In future 6G millimeter wave (mmWave)/sub-THz vehicle-to-everything (V2X) communication systems, vehicles are expected to be equipped with massive antenna arrays to realize beam-based links capable of compensating for the severe path loss. However, vehicle-to-vehicle (V2V) direct links are prone to be blocked by surrounding vehicles. Emerging metasurface technologies enable the control of the electromagnetic wave reflection towards the desired direction, enriching the channel scattering to boost communication performance. Reconfigurable intelligent surfaces (RIS), and mostly the pre-configured counterpart intelligent reflecting surfaces (IRS), are a promising low-cost relaying system for 6G. This paper proposes using conformal metasurfaces (either C-RIS or C-IRS) deployed on vehicles' body to mitigate the blockage impact in a highway multi-lane scenario. In particular, conformal metasurfaces create artificial reflections to mitigate blockage by compensating for the non-flat shape of the vehicle's body, such as the lateral doors, with proper phase patterns. We analytically derive the phase pattern to apply to a cylindrical C-RIS/C-IRS approximating the shape of the car body, as a function of both incidence and reflection angles, considering cylindrical RIS/IRS as a generalization of conventional planar ones. We propose a novel design for optimally pre-configured C-IRS to mimic the behavior of an EM flat surface on car doors, proving the benefits of C-RIS and C-IRS in a multi-lane V2V highway scenario. The results show a consistent reduction of blockage probability when exploiting C-RIS/C-IRS, 20% for pre-configured C-IRS, and 70% for C-RIS, as well as a remarkable improvement in terms of average signal-to-noise ratio, respectively 10-20 dB for C-IRS and 30-40 dB for C-RIS.

eess.SP↗

arXiv2021

Dual Domain Waveform Design for Joint Communication and Sensing Systems

The evolution of wireless communication systems towards millimeter-wave ($30-100$ GHz) and sub-THz ($>100$ GHz) frequency bands highlighted the need for accurate and fast beam management and a proactive link-blockage prediction in high-mobility scenarios. Joint Communication and Sensing (JC\&S) systems aim at equipping communication terminals with sensing capabilities using the same time/frequency/space communication resources to solve, or alleviate, the aforementioned issues. For an efficient implementation, a suitable waveform design that combines communication and sensing capabilities is of utmost importance. This paper proposes a novel dual-domain waveform design approach that superimposes onto the Frequency-Time (FT) domain both the legacy orthogonal frequency division multiplexing modulation scheme and a sensing signal, purposely designed in the Delay-Doppler (DD) domain. The power of the two signals is properly allocated in FT and DD domains, respectively, to reduce their mutual interference and optimize both communication and sensing tasks. Numerical results show the effectiveness of the proposed JC\&S waveform design approach, yielding target communication and sensing performance with a full time-frequency resource sharing.

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arXiv2021

Multi-Beam Automotive SAR Imaging in Urban Scenarios

Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving. Compared to lidars and cameras, automotive-legacy radars can work in any weather condition and without an external source of illumination, but are limited in either range or angular resolution. SARs offer a relevant increase in angular resolution, provided that the ego-motion of the radar platform is known along the synthetic aperture. In this paper, we present the results of an experimental campaign aimed at assessing the potential of a multi-beam SAR imaging in an urban scenario, composed of various targets (buildings, cars, pedestrian, etc.), employing a 77 GHz multiple-input multiple-output (MIMO) radar platform based on a mass-market available automotive-grade technology. The results highlight a centimeter-level accuracy of the SAR images in realistic driving conditions, showing the possibility to use a multi-angle focusing approach to detect and discriminate between different targets based on their angular scattering response.

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arXiv2021

Residual Motion Compensation in Automotive MIMO SAR Imaging

This paper deals with the analysis, estimation, and compensation of trajectory errors in automotive-based Synthetic Aperture Radar (SAR) systems. First of all, we define the geometry of the acquisition and the model of the received signal. We then proceed by analytically evaluating the effect of an error in the vehicle's trajectory. Based on the derived model, we introduce a motion compensation (MoCo) procedure capable of estimating and compensating constant velocity motion errors leading to a well-focused and well-localized SAR image. The procedure is validated using real data gathered by a 77 GHz automotive SAR with MIMO capabilities.

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arXiv2021

Vehicular Blockage Modelling and Performance Analysis for mmWave V2V Communications

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.

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arXiv2021

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

In the emerging high mobility Vehicle-to-Everything (V2X) communications using millimeter Wave (mmWave) and sub-THz, Multiple-Input Multiple-Output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time domain (i.e., directions or arrival/departure and delays). Algebraic Low-rank (LR) channel estimation exploits space-time channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles' geographical positions and tens to hundreds of training vehicles' passages for each position, leading to significant complexity and control signalling overhead. Here we design a DL-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single LS channel estimate and without needing vehicle's position information. Numerical results show that the proposed method attains comparable Mean Squared Error (MSE) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different space-time channel features, providing comparable MSE performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios.

cs.LG↗

arXiv2021

Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning

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 (U-ML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are suited to low-mobility scenarios and 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 are clustered via K-medoids algorithm based on their \textit{algebraic similarity} to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less).

cs.IT↗

arXiv2021

6G V2X Technologies and Orchestrated Sensing for Autonomous Driving

6G technology targets to revolutionize the mobility industry by revamping the role of wireless connections. In this article, we draw out our vision on an intelligent, cooperative, and sustainable mobility environment of the future, discussing how 6G will positively impact mobility services and applications. The scenario in focus is a densely populated area by smart connected entities that are mutually connected over a 6G virtual bus, which enables access to an extensive and always up-to-date set of context-sensitive information. The augmented dataset is functional to let vehicles engage in adaptive and cooperative learning mechanisms, enabling fully automated functionalities with higher communication integrity and reduced risk of accidents while being a sentient and collaborative processing node of the same ecosystem. Smart sensing and communication technologies are discussed herein, and their convergence is devised by the pervasiveness of artificial intelligence in centralized or distributed and federated network architectures.

cs.NI↗

arXiv2021

Channel Estimation for 6G V2X HybridSystems using Multi-Vehicular Learning

Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves (mmW)/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage \textit{recurrent vehicle passages} to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable Spectral Efficiency (SE) and Mean Square Error (MSE) compared to the Unconstrained Maximum Likelihood (U-ML) estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal Fully Digital (FD) systems.

cs.IT↗

arXiv2021

Sensor-Aided Beamwidth and Power Control for Next Generation Vehicular Communications

Ultra-reliable low-latency Vehicle-to-Everything (V2X) communications are needed to meet the extreme requirements of enhanced driving applications. Millimeter-Wave (24.25-52.6 GHz) or sub-THz (>100 GHz) V2X communications are a viable solution, provided that the highly collimated beams are kept aligned during vehicles' maneuverings. In this work, we propose a sensor-assisted dynamic Beamwidth and Power Control (BPC) system to counteract the detrimental effect of vehicle dynamics, exploiting data collected by on-board inertial and positioning sensors, mutually exchanged among vehicles over a parallel low-rate link, e.g., 5G New Radio (NR) Frequency Range 1 (FR1). The proposed BPC solution works on top of a sensor-aided Beam Alignment and Tracking (BAT) system, overcoming the limitations of fixed-beamwidth systems and optimizing the performance in challenging Vehicle-to-Vehicle (V2V) scenarios, even if extensions to Vehicle-to-Infrastructure (V2I) use-cases are feasible. We validate the sensor-assisted dynamic BPC on real trajectories and sensors' data collected by a dedicated experimental campaign. The goal is to show the advantages of the proposed BPC strategy in a high data-rate Line-Of-Sight (LOS) V2V context, and to outline the requirements in terms of sensors' sampling time and accuracy, along with the end-to-end latency on the control channel.

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arXiv2019

RF-Assisted Free-Space Optics for 5G Vehicle-to-Vehicle Communications

Vehicle-to-Vehicle (V2V) communications are being proposed, tested and deployed to improve road safety and traffic efficiency. However, the automotive industry poses strict requirements for safety-critical applications, that call for reliable, low latency and high data rate communications. In this context, it is widely agreed that both Radio-Frequency (RF) technologies at mmWaves and Free-Space Optics (FSO) represent promising solutions, although their performances are severely degraded by transmitter-receiver misalignment due to the challenging high-mobility conditions. By combining RF and FSO technologies, this paper proposes a FSO-based V2V communication system where the pointing coordinates of laser sources are based on vehicle's information exchanged over a reliable low-rate RF link. Numerical simulations demonstrate that such compensation mechanism is mandatory to counteract the unavoidable misalignments induced by vehicle dynamics, and thus to enable FSO technology for V2V communications even in high mobility scenarios.

cs.IT↗