Raffaele D Errico

The massive scale of Internet of Things (IoT) connectivity expected in 6G networks raises unprecedented challenges in energy use, battery waste, and lifecycle sustainability. Current cellular IoT solutions remain bound to the lifetime of underlying network generations and rely on billions of disposable batteries, creating unsustainable economic and environmental costs. This article proposes generation-agnostic zero-energy devices (XG-ZEDs), a new class of backscatter based IoT devices that are battery-less, spectrum-agnostic, and future-proof across successive network generations. XG-ZEDs exploit existing ambient wireless signals for communication, sensing, and localization, transforming infrastructure and user devices into universal enablers of ultra-low-power connectivity. We review architectural classifications, communication protocols, network integration, and representative applications such as sensing, localization, and radio-SLAM, while outlining the challenges ahead.

The Future Railway Mobile Communication systems (FRMCS) is the new system under development, based on 5G-NR, that will replace the GSM-R standard. Its deployment will require the design of new radio engineering tools, which in particular rely on new 3D radio channel models to validate the performance of proposed networking technologies under high-speed conditions, and to perform future radio-planning tasks. The 5GREMORA project aims at developing new measurement-based 3D radio channel models for railways, and demonstrating how new scheduling algorithms can be assessed with those models. The paper outlines the main ambitions of the project; and presents the preliminary results obtained so far.

Reconfigurable Intelligent Surfaces (RISs) are expected to play a pivotal role in future indoor ultra high data rate wireless communications as well as highly accurate three-dimensional localization and sensing, mainly due to their capability to provide flexible, cost- and power-efficient coverage extension, even under blockage conditions. However, when considering beyond millimeter wave frequencies where there exists GHz-level available bandwidth, realistic models of indoor RIS-parameterized channels verified by field-trial measurements are unavailable. In this article, we first present and characterize three RIS prototypes with unit cells of half-wavelength intercell spacing, which were optimized to offer a specific nonspecular reflection with 1-, 2-, and 3-bit phase quantization at 304 GHz. The designed static RISs were considered in an indoor channel measurement campaign carried out with a 304 GHz channel sounder. Channel measurements for two setups, one focusing on the transmitter-RIS-receiver path gain and the other on the angular spread of multipath components, are presented and compared with both state-of-the-art theoretical models as well as full-wave simulation results. The article is concluded with a list of challenges and research directions for RIS design and modeling of RIS-parameterized channels at THz frequencies.

In this paper, we present the results of a measurement campaign conducted in an indoor environment with multiple Reconfigurable Intelligent Surfaces (RISs), including a transmitting and a reflecting one in the Ka band. On the receiver side, a virtual array of monopole antennas is considered. The reconfiguration capabilities of both binary RISs are validated in various configurations, including different incidence/reflection angles and distances. A Multi Path Components (MPCs) extraction is performed in order to highlight the impact of RIS within a rich channel. Based on these results, we experimentally validated a general free-space multiple-input multiple-output (MIMO) channel transfer function with an arbitrary number of cascaded RISs, in either transmitting or reflecting modes, as well as a radar-range equation model.

Next-generation wireless networks will see the convergence of communication and sensing, also exploiting the availability of large bandwidths in the Terahertz (THz) spectrum and electrically large antenna arrays on handheld devices. In particular, it is envisaged that user devices will be able to automatically scan their surroundings by steering a very narrow antenna beam and collecting echoes reflected by objects and walls to derive a map of indoors and infer users' trajectories using simultaneous localization and mapping (SLAM) techniques. In this paper, we address this scenario by proposing original radio- SLAM (R-SLAM) algorithms, derived from image processing techniques, to map the environment and pinpoint the device position in the map starting from measurements sensed by a mobile THz radar. Initially, to fully understand the THz backscattering phenomenon, we provide an experimental characterization of the THz backscattering channel in indoor environments. Then, the performance of the proposed algorithms is assessed using realworld THz radar measurements and is compared with state-ofthe- art SLAM techniques, demonstrating the superiority of the proposed approaches.

In this work, we study hybrid precoding techniques applied to multi-user Transmitting Reconfigurable Intelligent Surface (T-RIS) systems. The T-RIS considered here is a large array of electronically reconfigurable antenna elements illuminated by a small set of active sources. When it comes to digital signal-processing techniques applied to T-RIS systems, it is necessary to consider realistic models to bridge the gap with theoretical results. For this reason, we propose a multi-beam T-RIS propagation model with strong phase quantization constraints and limited beam codebooks. First, the proposed model is validated by characterizing a Ka-band T-RIS. Then, we optimize the quad-beam T-RIS structure by tuning the focal distance between the lens and the focal sources according to two metrics: (i) the per-user antenna gain (analog-only approach), and (ii) the per-user average rate (hybrid digital/analog approach). For both indicators, the system performance is evaluated in a multi-user scenario by assuming imperfect channel state information. We show that considering only the analog precoder is sufficient to optimize the T-RIS. However, the fully hybrid precoding scheme is required to deal with inter-user interference. We propose a codebook-aware optimization that improves the aperture efficiency of the T-RIS system.

Next-generation wireless networks will see the convergence of communication and sensing, also exploiting the availability of large bandwidths in the Terahertz (THz) spectrum and electrically large antenna arrays on handheld devices. In particular, it is envisaged that user devices will be able to automatically scan their surroundings by steering a very narrow antenna beam and collecting echoes reflected by objects and walls to derive a map of indoors and infer users' trajectories using simultaneous localization and mapping (SLAM) techniques. In this paper, we address this scenario by proposing original radioSLAM (R-SLAM) algorithms, derived from image processing techniques, to map the environment and pinpoint the device position in the map starting from measurements sensed by a mobile THz radar. Initially, to fully understand the THz backscattering phenomenon, we provide an experimental characterization of the THz backscattering channel in indoor environments. Then, the performance of the proposed algorithms is assessed using real-world THz radar measurements and is compared with state-of-the-art SLAM techniques, demonstrating the superiority of the proposed approaches.

In this paper, we present a multi-band channel characterization at sub-THz. Three measurement campaigns were conducted in an indoor environment, for different frequency ranges: 125-155, 235-265 and 270-300 GHz, by exploiting a single (vertical) polarization antennas and performing only the azimuth plane. Line-of-Sight and Non-Line-of-Sight, given by human body obstructions and wall are considered. From directional measurements, the omnidirectional power angular delay profile was extracted. Modeling of large-scale parameters and cluster study are presented.

In this article, we analyze the characteristics of indoor radio channels at millimeter waves. Measurements were performed using a large virtual antenna array (VAA), for massive multiple input multiple output (MIMO) applications. The results in terms of correlation and multipath components (MPCs) characteristics are analyzed for the whole large array and virtual subarrays composing it. Common MPC among the subarrays are identified, and the impact on the overall channel large-scale parameters is discussed.