This paper presents a novel game-theoretic approach for efficient resource allocation in Integrated Access and Backhaul (IAB). We model the interactions between multiple decision-makers as a strategic game, defining the strategy spaces and utility functions for the involved players. By employing potential game theory, we discuss the existence of a Nash equilibrium and propose an iterative algorithm to achieve it. Our proposed solution is evaluated against two baselines, namely Soft Actor Critic (SAC) and Max-Rate Backlog Avoidance (MRBA) demonstrating superior performance in resource efficiency and decision-making precision. These findings offer valuable insights for designing scalable and efficient network management frameworks.

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.

Early Exiting (EE) is an emerging computing paradigm where Deep Neural Networks (DNNs) are equipped with earlier classifiers, enabling trading-off accuracy with inference latency. EE can be effectively combined with edge computing, a paradigm that allows mobile nodes to offload complex tasks, such as the execution of DNNs, to servers at the edge of the network, thus reducing computing times and energy consumption at the mobile devices. The integration of such technologies is particularly attractive for the support of applications for connected and automated driving. In this paper, we consider a system that jointly leverages the benefits of EE and edge computing, and we model their complex interactions by means of a Markov Decision Process (MDP). We then formulate an optimization problem to select the inference strategy that maximizes the average task accuracy. Importantly, such an optimization problem has low complexity, as the optimal policy can be derived by mapping the MDP into a linear program. Our numerical results focus on a use case centered on automated vehicles connected with an edge server under varying channel and network conditions, and show that our solution achieves up to 11% higher accuracy compared to the optimal policy with no EE.

Intelligent Reflective Surfaces (IRSs) enable controlling the radio propagation environment to enhance wireless communications and are particularly relevant for vehicle-to-everything (V2X) applications. This paper explores the integration of IRSs for virtual line-of-sight (VLOS) propagation in urban vehicle platoons that operate with New Radio (NR)-V2X mode 2 sidelink communications and proposes a semi-analytical framework to assess the performance of such systems. The framework considers the availability of resources, the number of interference vehicles, and their relative positions. A Nakagami-Lognormal composite channel model is assumed to describe the propagation environment. This approach provides a cost-effective and accurate estimate of packet success rates, facilitating optimal configuration and parameter adjustments for vehicle platoons. We demonstrate that under typical inter-vehicle distances and with large numbers of vehicles, VLOS communication via IRS can even surpass the performance of conventional Line-of-Sight (LOS) scenarios.

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 ofcapture diversityand 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.

In this paper, we propose a model for the analysis of distributed passive backscattering systems for Radio Frequency Identification with physically separated illuminator and reader. The model takes into account the physical channel characteristics including shadow fading components and thresholds for powering and detection. The dynamic framed ALOHA medium access mechanism defined by the EPC Global Generation 2 standard is considered to evaluate the performance. To address the complexity of the system model, our framework is based on a semi-analytical approach that combines moment matching approximation method at channel level and Monte-Carlo approach for the medium access control dynamics. Results show the impact of deployment conditions and the relative positions among illuminator, tags, and reader on the identification performance. Interestingly, the detection of the back-scattered signal at a remote reader can improve the performance with a higher probability of capture. However, as the distance increases, the impact of shadow fading counterpoises the capture effect. The resulting trade-off is accurately described by our model.

In this paper, we propose an equivalent channel model for the analysis of passive backscattering systems (e.g., Radio Frequency Identification systems). The proposed framework accurately models radiation efficiencies at the backscattering nodes whose communication channels are affected by spatially correlated shadowing. First, we derive the distribution of interference in the system and the probability that a node will activate as a function of a specific geographical distribution of the nodes. Then, we approximate the capture probability using a multivariate moment matching approach. The rationale is to provide an underlying structure for the cross-layer analysis of current MAC protocols with the perspective of performance enhancement. Numerical results illustrate the performance of a standard MAC protocol for passive RFID systems, including an accurate evaluation of the impact of the channel characterization.

Energy harvesting is a key technology for future energy neutral ubiquitous wireless communication systems. Among the many available sources of energy, RF harvesting is a natural choice for wireless communications since the devices can be efficiently designed for RF harvesting operations. To achieve energy autonomy a rechargeable energy storage is needed. However, the bursty nature of energy arrival associated with wireless power transfer and the energy usage pattern induced by devices' activity may cause considerable stress to the battery and reduce its life span. In fact, deep charging and discharging cycles degrade the battery state of health, that is, the maximum amount of energy that can be stored. In this paper, a framework for the optimization of wireless nodes' transmission and charging strategy is presented, where the battery aging rate is made explicit as a performance metric. The proposed framework is based on MDP theory, where the embedded stochastic model captures the energy arrival and data generation processes, and the control variable takes the form of a transmission power selection and energy acceptance/rejection. The tradeoff between the achievable throughput and the aging rate is explored for different parameters setting.

Energy harvesting is arising as a key technology in wireless systems, allowing continuous and prolonged operations. However, the bursty nature of the energy arrival process associated with renewable sources and the energy usage pattern caused by wireless protocols may cause considerable stress to the battery and eventually reduce its lifetime. In fact, deep charging and discharging cycles degrade the battery State of Health, that is, the maximum amount of energy that can be stored. In this paper, a framework for the optimization of wireless nodes' transmission strategy is presented, where battery aging rate is included as a constraint. The proposed framework is based on Markov Decision Process theory, where the embedded stochastic process models energy arrival and storage, and channel fading, as well as the control variables. Numerical results unveil the tension between packet delivery rate and battery degradation.

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.