The pinching-antenna systems (PASS), which activate small dielectric particles along a dielectric waveguide, has recently emerged as a promising paradigm for flexible antenna deployment in next-generation wireless communication networks. While most existing studies assume rectangular indoor layouts with full coverage waveguide, practical deployments may involve geometric constraints, partial coverage, and non-negligible waveguide attenuation. This paper presents the first analytical investigation of PASS in a circular indoor environment, encompassing both full coverage and partial coverage waveguide configurations with/without propagation loss. A unified geometric– propagation framework is developed that jointly captures pinching-antenna placement, Internet of Things (IoT) device location distribution, and waveguide attenuation. Closed-form expressions for the outage probability and average achievable rate are derived for four scenarios, with accuracy validated via extensive Monte-Carlo simulations. The analysis reveals that, under the partial coverage waveguide scenario with propagation loss, the system performance demonstrates a non-monotonic trend with respect to the waveguide length, and the optimal length decreases as the attenuation coefficient increases. Numerical results further quantify the interplay between deployment strategy, waveguide propagation loss, and coverage geometry, offering practical guidelines for performance-oriented PASS design.

This article explores the tracking issue for dc-dc buck power converter with stochastic disturbance, specifically focusing on how output voltage tracks to a desired voltage in a finite-time when the load changes. Meanwhile, considering unmodeled dynamics and nonaffine inputs, we propose an innovative finite-time fuzzy prescribed performance switching tracking algorithm to achieve tracking goal. For realizing the requirements of performance, the algorithm converts the tracking error to a new state by means of a coordinate transformation via the tangent function. In addition, the universal approximation capacity of the fuzzy-logic system is utilized to estimate the unknown nonlinear term effectively. On this basis, the designed adaptive dynamic event-triggered controller can not only ensure that all the signals for the closed-loop system remain bounded in probability but also guarantee that the tracking error will converge to a predetermined small neighborhood. Meanwhile, different piecewise functions are added into the controller to characterize prescribed performance and avoid singularity problems, respectively. Finally, the effectiveness of the tracking control algorithm is fully demonstrated by the simulations of the dc-dc buck power converter.

Intelligent reflecting surface (IRS) can significantly enhance the performance of data and energy integrated networks (DEIN) by adjusting its amplitude and/or phase. However, there is a lack of comprehensive performance analysis model for realistic DEIN where multiple cells exist rather than only one cell as assumed by most existing work. In this paper, we consider an IRS-assisted multi-cell DEIN. Specifically, in the downlink wireless energy transfer (WET) stage, the hybrid access point (HAP) in each cell broadcasts radio frequency (RF) energy signals to edge user equipments (UEs). Subsequently, during the uplink wireless information transfer (WIT) stage, the edge UEs employ the harvested energy to send their information to the HAP. We first represent the statistical characteristics of the signal-to-interference-plus-noise ratio (SINR) at the edge UE. Then, we derive the closed-form expressions for outage probability, ergodic rate and average symbol error probability of the edge UE in the typical cell. To gain more insights, we obtain the minimum required number of reflection elements and a sub-optimal solution for time allocation coefficients. Finally, extensive numerical results are provided to validate the correctness of the theoretical results.

Compared to traditional passive reconfigurable intelligent surfaces (RISs), active RISs can actively amplify incident signals, enhancing signal strength, reducing path-loss, and extending coverage. In this paper, we investigate an active RIS-assisted full-duplex integrated sensing and communication (ISAC) system. By jointly designing the power allocation factor for communication user equipment (UE) and the reflecting coefficients of the active RIS, the long-term average sensing performance is maximized while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for all UEs. In this paper, we explore a deep reinforcement learning (DRL) algorithm to address the complex non-convex optimization problem, aiming to maximize long-term gains in system sensing performance. Simulation results indicate that the soft actor-critic (SAC) algorithm achieves superior performance compared to two benchmark algorithms, and that active RIS demonstrates enhanced capabilities over traditional RIS.

Reconfigurable intelligent surface (RIS) is a promising technology to improve the spectral and energy efficiency of Internet of Things (IoT) systems. In this paper, we investigate an RIS-assisted simultaneous wireless information and power transmission (SWIPT) system by utilizing stochastic geometry. Moreover, we consider not only the case of random phase shift, but also the case where the phase shift of the RIS are aligned to thek-th IoT device. We first derive the closed-form expressions of the uplink outage probability and the average uplink data size for thek-th IoT device under the Rayleigh channel. Then, we extend the performance analysis to the Rician fading channel and multi-antenna scenarios. Finally, extensive numerical results have been carried out to verify the effectiveness of our derived results.

Empowering sensing capabilities is becoming increasingly important in future wireless networks. Meanwhile, intelligent reflecting surface (IRS) and wireless power transfer (WPT) have also received widespread attention as two key technologies to improve network spectrum efficiency and solve device energy shortage issues, respectively. To this end, we investigate an IRS-assisted and WPT-enabled integrated sensing and communication (ISAC) system. Specifically, a base station (BS), with the assistance of the IRS, has the dual functions of radar sensing as well as receiving the data information transmitted by Internet of Things (IoT) devices. IoT devices can charging itself by harvesting the power of radar signals transmitted from the BS. The sensing performance is studied by deriving an exact closed-form expression and an upper bound of the average radar estimation information rate. In addition, we derive an exact expression for the average data information rate to evaluate the communication performance of the system. The simulation results reveal that increasing the number of reflecting elements of the IRS can simultaneously enhance the radar sensing and communication performance.