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.

Developing radio access technologies that enable reliable and low-latency vehicular communications have become of the utmost importance with the rise of interest in autonomous vehicles. The Third Generation Partnership Project (3GPP) has developed Vehicle to Everything (V2X) specifications based on the 5G New Radio Air Interface (NR-V2X) to support connected and automated driving use cases, with strict requirements to fulfill the constantly evolving vehicular applications, communication, and service demands of connected vehicles, such as ultra-low latency and ultra-high reliability. This paper presents an analytical model for evaluating the performance of NR-V2X communications, with particular reference to the sensing-based semi-persistent scheduling operation defined in the NR-V2X Mode 2, in comparison with legacy sidelink V2X over LTE, specified as LTE-V2X Mode 4. We consider a vehicle platooning scenario and evaluate the impact of multiple access interference on the packet success probability, by varying the available resources, the number of interfering vehicles, and their relative positions. The average packet success probability is determined analytically for LTE-V2X and NR-V2X, taking into account the different physical layer specifications, and the Moment Matching Approximation (MMA) is used to approximate the statistics of the signal-to-interference-plus-noise ratio (SINR) under the assumption of a Nakagami-lognormal composite channel model. The analytical approximation is validated against extensive Matlab simulations that a show good accuracy. The results confirm a boost in performance with NR-V2X against LTE-V2X, particularly for high inter-vehicle distance and a large number of vehicles, providing a concise yet accurate modeling rationale for planning and adaptation of the configuration and parameter setup of vehicle platoons, without having to resort to extensive computer simulation or experimental measurements.

In this paper, we propose a framework for cross-layer analysis of multi-static passive RFID systems. The model takes into account details of the shared wireless channel, including fading and capture effect, whereas, at the medium access control (MAC) layer, the anti-collision mechanism proposed in the EPC Generation 2 standard is taken as a reference. To address the complexity of the system model, we rely on a semi-analytical approach, that combines a moment matching approximation method to abstract the physical layer and Monte-Carlo simulations to describe the MAC dynamics. Furthermore, based on the space diversity feature offered by the multi-static settings, we introduce the concept of capture diversity and propose a modification to the standard to fully support this form of diversity. Numerical results show the impact of deployment conditions and the relative positions of interrogator, tags, and detection points on the performance of tags’ identification. We show how the number of detection points impacts the system performance under various channel conditions and MAC parameters’ settings. Finally, we validate the proposed update of the MAC protocol, showing substantial performance improvement with respect to the standard collision resolution policy.

Within the overall framework of the GRETA project, this contribution proposes a novel protocol architecture that efficiently integrates UHF and UWB radio components in order to improve the performance of identification algorithms in large scale deployments that potentially involve large populations of tags and multiple readers. We move from our recent work, where we have shown that an UWB-enhanced architecture, that guarantees full backward compatibility with ISO/IEC 18000- 6C UHF standard, can improve by approximately 25% the performance in terms of success rate and energy consumption. The second step is concerned with the proposal of a further tag identification protocol, that is based on compressive sensing (CS) and UWB: the main feature of the proposed scheme is that it exploits UWB information to reduce the ID search space of CS decoding. This hybrid technique is still oriented to further decrease the speed in tag identification, while reducing the computational complexity if compared to an UHF-RFID only protocol. Finally, we extend our approach to the case of multiple readers, that do not only improve coverage, but can also further improve the performance indexes: in this case a cooperative approach is proposed in order to also manage readertag collisions and reader-reader collisions other than the typical tag-tag collisions.

Energy harvesting is becoming a key technology in mobile wireless networks, and especially sensor systems. The bursty nature of energy arrival generated by renewable resources may apply considerable stress to the battery, and degrade its state of health (SoH) by generating deep charging and discharging cycles. In this letter, a novel random channel access scheme is proposed that tunes transmission parameters to reduce SoH degradation while preserving the network performance.

Energy harvesting from environments such as solar energy are promising solutions to tackle energy sustainability in embedded systems. However, uncertainties in energy availability, non-ideal characteristics of harvesting circuits, energy storage (battery or supercapacitor), and application demand dynamics add more complexity in the system. We present a unified model based on discrete-time Finite State Markov Chain to capture the dynamicity and variations in both the energy supply from solar irradiance and the energy demand from the application. In this paper, we exploit the temporal and spatial characteristics of solar energy and propose a deterministic profile with stochastic process to reflect the fluctuation due to unexpected weather condition. Optimal policy to maximize expected total QoS is derived from the presented model using a probabilistic dynamic programming approach. Compared to a state-of-the-art deterministic energy management framework, our proposed approach outperforms in term of QoS and energy sustainability (with less shutdown time) of the system.