Abstract
Reconfigurable Intelligent Surfaces (RIS) are an enabling technology widely investigated towards 6G. The viability of large active metasurfaces is constrained by the RF performance, cost, and power consumption. The number of switches per unit cell is a key design parameter that designers aim to minimize following cost and power consumption drivers. However, an efficient use of the aperture is ultimately required and although a one-to-one correspondence between number of switches and phase-quantization bits seems intuitive, one may question its impact. Here we present a full-wave evaluation of a 30x30 1-bit reflective RIS, implemented considering two pin diodes per unit cell. The RIS allows scanning up to 60 degrees from 28 to 29 GHz with a maximum aperture efficiency of 22%. This superior performance provides tantalizing evidence that the multiple switches per bit approach should not be discarded a priori due to its apparent higher complexity.

Abstract
Wireless communications in the THz frequency band is an envisioned revolutionary technology for sixth Generation (6G) networks. However, such frequencies impose certain coverage and device design challenges that need to be efficiently overcome. To this end, the development of cost- and energy-efficient approaches for scaling these networks to realistic scenarios constitute a necessity. Among the recent research trends contributing to these objectives belongs the technology of Reconfigurable Intelligent Surfaces (RISs). In fact, several high-level descriptions of THz systems based on RISs have been populating the literature. Nevertheless, hardware implementations of those systems are still very scarce, and not at the scale intended for most envisioned THz scenarios. In this paper, we overview some of the most significant hardware design and signal processing challenges with THz RISs, and present a preliminary analysis of their impact on the overall link budget and system performance, conducted in the framework of the ongoing TERRAMETA project.

Abstract
Reconfigurable Intelligent Surfaces (RISs) are in significant focus within 6G research. However, RISs face a power consumption challenge in the reconfigurable elements which may restrict its future scale-up to large areas. We address this issue by proposing a unit cell based on a non-volatile memristor-based switching mechanism. A 1-bit memristor-based reconfigurable RIS unit cell was designed in the Ka-band, and validated using CST and HFSS simulation platforms. The required control circuit to enable the digital control of the memristor has also been proposed. The proposed unit cell achieves losses of less than 1 dB over a frequency band of 25 - 28.3 GHz and a phase difference of 180 degrees +/- 20 degrees at a central frequency of 26.7 GHz, with an operational bandwidth of approximately 1 GHz. Furthermore, an exemplary 16x16 RIS was designed and simulated based on the proposed unit cell to demonstrate its capability to achieve beam steering.

Abstract
This paper presents SpecRF-Posture, a novel low-cost approach for accurate Human Posture Recognition (HPR) using Radio Frequency (RF) signals. SpecRF-Posture leverages S21 parameters within the WiFi-6E frequency range for classification. We obtain a dataset of S21 parameters for different postures by performing beamscanning through mechanical rotation of a horn transmitter aimed at a reflective surface that illuminates the space of interest. We determine the S21 parameters of the signals that are then reflected back from the space onto an omni-directional receiver. Thus for each posture we attain the S21 parameters of each possible illumination direction of the space. Experimental results demonstrate that SpecRF-Posture achieves an accuracy of 99.17% in posture classification, highlighting its effectiveness. Additionally, an RF dataset was acquired using a software package for automatic data acquisition within the WiFi-6E frequency range, and both the dataset and the software package have been made publicly available.

Abstract
In the domain of RIS-based indoor localization, our work introduces two distinct approaches to address real-world challenges. The first method is based on deep learning, employing a Long Short-Term Memory (LSTM) network. The second, a novel LSTM-PSO hybrid, strategically takes advantage of deep learning and optimization techniques. Our simulations encompass practical scenarios, including variations in RIS placement and the intricate dynamics of multipath effects, all in Non-Line-of-Sight conditions. Our methods can achieve very high reliability, obtaining centimeter-level accuracy for the 98th percentile (worst case) in a different set of conditions, including the presence of the multipath effect. Furthermore, our hybrid approach showcases remarkable resolution, achieving submillimeter-level accuracy in numerous scenarios.

Abstract
This paper presents an optically transparent 2-bit unit-cell for reflective intelligent surface applications in Wi-Fi 6E. The unit-cell is based on a metalmesh and can be reconfigured electronically by adjusting the voltage applied to a varactor diode. The performance of the RIS is demonstrated through simulation, which shows that the results are in good agreement with the theoretical predictions.