Christian Mazzucco

Numerical evaluation and/or measurement campaigns are currently the only means of evaluating the isolation between antennas; in the former case, the simulation time and computational resources necessary can be burdensome, whereas the latter requires several weeks of work to be completed. Within this context, this article aims at presenting an effective methodology for the evaluation of the isolation between co-located antennas in a rapid computational time. The proposed method is based on the near-field equivalent representation of real antennas ( Tx and Rx ) combined with the ray tracing method for evaluating the field re-radiated by the source antenna on the receiver, hence the received power. The procedure is general and suitable to any type of antenna with any configuration and number of radiation elements. Moreover, antenna mechanical and electrical details are not needed, since only near field information is needed. Finally, it considers the presence of the support structure and surrounding obstacles. The methodology has been validated by full-wave simulations and measurements, yielding consistent and accurate results with an error that is below 2 dB in most of the cases, up to 6 dB for very low coupling values, indicating a high level of accuracy and reliability.

A 16 switched beam lens based antenna with a coverage area of $\pm 30 ^\circ$ in the horizontal plane and from 0 to $-20 ^\circ$ in the vertical plane has been built and tested. The intended use of the antenna is as part of a smart repeater servicing the coverage area of a 5G wireless communication system operating in the n258 band of the 5G Frequency Range 2, from 24.25 to 27.5 GHz. The antenna has been built using state-of-the-art materials and components in order to assess the performance of this antenna concept. The overall antenna losses including the feeding network are of the order of 8 dB and the realized gain in the $80 \%$ of the coverage area is above 11 dB.

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 6 GHz (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 toward the geostationary (GEO) SATs, is well below the relevant protection criterion, and we prove numerically that the GSMI method overestimates the aggregated interference power with only 2 dB compared to the SMI method.

Buildings and vegetation in the proximity of a terrestrial base station induce a significant additional loss, typically referred to as “clutter loss”, which sums up to free space loss and atmospheric attenuation. Clutter loss is essentially due to the radiowave reflection and diffraction caused by buildings and vegetation, and tends to reduce the interference between terrestrial systems, such as upper 6 GHz (U6G), and satellite systems operating in the same frequency bands. In fact, for example, at low elevation angles, the clutter loss could reach some tens of dB in the U6G band. A novel clutter loss model in urban and suburban environments for frequencies up to 10 GHz is proposed. The model relies on the Monte Carlo simulation approach presented in Report ITU-R P.2402-0, but some limitations have been removed to extend its applicability to more complex scenarios and possibly increase its accuracy for U6G systems. An analytical approach is also proposed to model the clutter loss statistics obtained by properly fitting the obtained statistics for the cities of London and Melbourne. Finally, the proposed model is validated by comparing its results to those obtained by a commercial ray tracer.

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-called autofocus algorithms. 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 autofocus requirements in 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 this paper, we discuss the possibility of generating high-resolution mapping of urban (or extra-urban) environments by the application of synthetic aperture radar (SAR) processing concepts to the data collected by mm-wave automotive radars installed on-board commercial vehicles. The study is motivated by the fact that radar sensors are becoming an indispensable component of the equipment of modern vehicles, being characterized by low cost, good performance, and affordable processing; therefore, in the future, nearly every single vehicle could be potentially equipped with radar devices capable of high-resolution imaging, enabled by application of SAR processing methodologies. Throughout this paper, we aim to discuss the role of SAR imaging in the automotive context under a theoretical and experimental perspective. First, we present the resulting benefits in terms of angular resolution and signal-to-noise ratio. Then, we discuss relevant technological aspects, such as suppression of angular ambiguities, fine estimation of platform motion, and SAR processing architectures, and we present a preliminary evaluation of the required computational costs. Finally, we will present a number of experimental results based on open road campaign data acquired using an 8-channel MIMO radar at 77 GHz, considering the cases of side-looking SAR, forward SAR, and SAR imaging of moving targets.

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.

A new sub-6 GHz antenna array that supports slant ±45. dual-polarization Multiple-Input-Multiple-Output (MIMO) communications with high inter-port isolation and stable radiation performance over wide scan angles and bandwidths is designed. An architecturally-inexpensive solution, which combines a standard truncated waveguide array and a single-layer patterned metasurface superstrate with wave manipulation capabilities, is proposed. A task-oriented formulation is adopted to mathematically formulate the EM associated multi-scale design problem, then solved with an innovative instance of the System-by-Design (SbD) paradigm where the fast and reliable modeling of the device is yielded by an ad-hoc artificial intelligence based surrogate. The final layout of the antenna array is validated with industry-standard full-wave numerical simulators.

Fifth generation (5G) and beyond communication systems open the door to millimeter Wave (mmWave) frequency bands to leverage the extremely large operating bandwidths and deliver unprecedented network capacity. These frequency bands are affected by high propagation losses that severely limit the achievable coverage. The simplest way to address this problem would be to increase the number of installed mmWave base stations (BSs), at the same time augmenting the overall network cost, power consumption and ElectroMagnetic Field (EMF) levels. As alternative direction, here we propose to complement the macro-layer of mmWave BSs with a heterogeneous deployment of Smart Electromagnetic (Smart EM) Entities – namely IAB nodes, Smart Repeaters, Reconfigurable Intelligent Surfaces (RISs) and passive surfaces – that is judiciously planned to minimize the total installation costs while at the same time optimizing the network spectral efficiency. Initial network planning results underline the effectiveness of the proposed approach. The available technologies and the key research directions for achieving this view are thoroughly discussed by accounting for issues ranging from system-level design to the development of new materials.