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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.

Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves/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 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 and mean square error compared to the unconstrained maximum likelihood 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 systems.

The beam sweeping procedure estimates the beamforming directions, or beams, to be used for downlink millimeter-wave cellular transmissions to an incoming user entering the cell. We propose three new approaches for the choice of the sequence by which beams are explored (the beam-search sequence), in order to reduce the average time to find the directions. The proposed methods exploit a) the correlation among angles of departure of the incoming user and its nearest, already connected user, and b) the position of both the incoming and its nearest user. For point a), the nearest-neighbor-beam search method starts from the beam of the nearest user, and then explores other beams, with directions progressively farther away (in the angular domain) from the first beam. In the beam-and-angle search, the beam-search sequence is optimized by exploiting the knowledge of both users' position and the nearest-user beam. We also propose the simplified beam-and-angle search, which still exploits the nearest-user information, although in a suboptimal and simpler fashion. Based on the channel model of the 3rd generation partnership project, we analytically design the optimal beam-and-angle search sequence. We assess the performance of the proposed solutions, in terms of their average discovery time, also in comparison with existing approaches.

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.