Fortunato Santucci

This letter presents a framework for designing optimal state-feedback control that uses a wireless actuation link with imperfect channel state information to transfer the current and future control inputs that actuators can apply if future control messages are lost. The dropout compensation strategy supports scaling inputs to actuators when necessary. We analytically solve finite-and infinite-horizon control problems and present a necessary and sufficient stability condition for any given infinite-horizon state-feedback control law. We validate the results using an illustrative example.

Nowadays the modern progress in wireless communication technologies is motivating the mutual integration of multiple services, in particular enabling resource and hardware-saving in platform deployment. In this perspective, Integrated Sensing and Communications (ISAC) is emerging as one of the most promising areas of research, especially for sensing technologies that are not originally designed for communication purposes, such as radar and Synthetic Aperture Radar (SAR). Current approaches leverage code modulation techniques through specific ground targets, which enable communication integration within SARs without requiring hardware modification. Besides, code transparency reduces the contribution of the target with low additive interference into the final SAR images. However, ISAC testing in real SAR systems poses numerous challenges. This paper introduces a novel testing framework aimed at demonstrating the effectiveness of communication embedding in SAR systems without the operational constraints of real-time tests. The proposed framework offers flexibility by supporting different SAR platforms, operational parameters, and acquisition modes, thus enabling comprehensive emulation of target backscattering. This approach provides a practical, scalable, and cost-effective solution for validating ISAC functionalities, which is crucial for next-generation communication and sensing systems. To validate the proposed testing framework, real raw data from the ESA Sentinel-1 constellation have been employed to emulate ISAC target behaviour, demonstrating the effectiveness of the framework.

This letter addresses a fundamental issue of time-varying parametric uncertainty affecting unreliable communication links that convey the control commands to actuators in wireless networked control systems. It introduces the polytopic time-inhomogeneous finite-state Markov channel model to account for significant changes in possible propagation channel characteristics and analytically solves the linear quadratic regulation problem under the generalized packet dropout compensation. An example validating the results is presented.

Time-domain macromodeling represents an effective technique for generating compact models of the linear time-invariant portion of complex electromagnetic systems. Among the various techniques, a well-known one involves the computation of the port impulse response at the system’s ports, which is then used for calculating convolution integrals. Such computation is typically slow. Recently, a method has been proposed to accelerate such computation by exploiting a piecewise constant approximation of the impulse responses, leading to the so-called segment fast convolution approach. The contribution of the present work is twofold: i) to extend the aforementioned method to the case of piecewise linear approximation of the port impulse responses in the case of linear time-invariant circuits; ii) to define a highly efficient quasi-recursive convolution computation algorithm for both constant and linear piecewise approximations. Moreover, vectorization is exploited to further improve computational efficiency. The proposed convolution schemes are validated in the case of a simple two-port network and then applied to relevant case studies of large-scale electromagnetic systems. Specifically, Reconfigurable Intelligent Surfaces (RISs) and neuromorphic circuits are modeled and validated. The numerical evaluation shows that the proposed schemes are able to guarantee high accuracy together with significant computational speed-ups.

This paper proposes an analytical framework for co-designing wireless networked control systems (WNCSs) demonstrated in an industrial scenario. The framework allows us to quantitatively characterize the impact of wireless channel model accuracy when designing a controller to stabilize a WNCS. We consider a scenario consisting of two co-located wireless networks: the first connects the plant automation network backbone to field devices via WirelessHART, ISA-100.11a, or IEEE 802.15.4e, and the second uses IEEE 802.11 equipment to supply real-time multimedia data to the supervisory devices. First, we derive a parametric 802.11 interference characterization for an arbitrary number of active interfering devices and perform extensive parametric analysis. We then derive the message and packet error probability expressions necessary to develop an appropriate finite-state Markov channel model. Finally, we ran sizeable Monte Carlo simulations to evaluate the impact of our channel model on the control performance of a wireless closed-loop system and compared it with the performance obtained using a Bernoulli channel model.

While data quality and quantity directly affect the estimation accuracy of deep learning (DL)-based localization techniques, obtaining empirical position data is often costly due to time-consuming experiments, especially for large-scale indoor environments. This article proposes a novel data collection method to build DL-based localization models for indoor vehicle navigation services using multidirectional beacons. By defining the directed graph of the model applicability, we show that high-resolution position labeling data of a limited number of beacons is sufficient to train the localization model for the whole indoor scenario. However, obtaining the ideal directed graph of the model applicability still requires costly high-resolution empirical data from all beacons. We thus propose an autoencoder-based predicted graph method using only cost-effective low-resolution proximity data. We derive the minimum set of beacons needed to collect high-resolution empirical data to train the localization model. We evaluate the proposed beacon selection method in terms of the estimation accuracy and the data collection cost to the beacon-specific model and the ideal directed graph-based method through the extensive set of experimental measurements. The evaluation results show that the proposed localization model using the autoencoder-based predicted graph provides good estimation accuracy while considerably reducing the required training beacons.

While technological advancements and their deep integration in connected and automated vehicles is a central aspect in the evolving trend of automotive industry, they also depict a growing size attack surface for malicious actors: the latter ones typically aim at exploiting known and unknown security vulnerabilities, with potentially disastrous consequences on the safety of vehicles, people, and infrastructures. In recent years, remarkable efforts have been spent to mitigate security vulnerabilities in intelligent and connected vehicles, in particular in the inside of vehicles, the so-called intra-vehicle networks. Despite those efforts, securing intra-vehicle networks remains a non-trivial task due to their heterogeneous and increasingly complex context. Starting from the above remarks and motivated by the industrial research and innovation project EMERGE, in this paper we report on a novel cryptographic hardware-software solution that we have designed and developed for securing the intra-vehicle network of intelligent connected vehicles: the Crypto-Engine. The Crypto-Engine relies on a lightweight hybrid-key cryptographic scheme to provide confidentiality and authentication without compromising the normal communication performance. We tested the Crypto-Engine and demonstrated that, once configured according to application-defined performance requirements, it can authenticate parties and secure the communications with a negligible overhead.

Reconfigurable intelligent surfaces (RISs) are planar, almost 2-D, structures that can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RISs are considered a potential enabling technology for the sixth-generation (6G) wireless communication systems due to their capability to tune wireless signals, thus smartly controlling propagation environments. This work has a twofold objective: firstly, we present a systematic methodology for characterizing RISs in the time domain, which rigorously includes propagation delays and mutual coupling among RIS elements; secondly, we analyze the RISs through a convolution-based solver that is enriched by a specialized convolution scheme that enables the analysis of systems with complex linear, nonlinear as well terminations. The proposed approach is validated by extensive numerical evaluations against well-established methods based on both frequency- and time-domain analyses, with a varying number of RIS elements, interdistance, and frequency of operation.