In this paper, we present the design of nanostructured multilayer absorbers, carried out with the aid of a genetic algorithm (GA). Waveguide measurements are performed to recover the dielectric properties of micrographite single-walled carbon nanotube, micrographite walled carbon nanotube, carbon nanofiber, and fullerene-based composite materials. Conductive fillers are uniformly dispersed in an epoxy resin at different weight percentages (1, 3, 5 wt.%). The electromagnetic (EM) analysis is performed embedding the forward/backward propagation matrix formalism in an in-house GA, thus able to carry out optimization upon oblique incidence over a finite angular range. Developed code minimizes both the reflection and the transmission coefficients under the thickness minimization constraint. Comparison between micrographite and nanopowders absorbers is presented and discussed, when a broadband quasi-perfect absorber is achieved among the X-band combining the two filler families, i.e., exhibiting a loss factor greater than 90% in most of the band, for a thickness of about 1 cm. It is demonstrated that the nanofillers with higher aspect ratio mainly contribute to the EM absorption. Findings are of interest in both radar-absorbing material and shielding structures.

This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications.