This work provides a comprehensive performance analysis of a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communication system in the presence of phase errors. We first derive the exact expressions for the first and second moments of the maximum signal-to-noise ratio at the receiver. We then use these moments to obtain highly accurate and insightful expressions for the outage probability, average symbol error probability (SEP), and ergodic rate (ER) for various phase error distributions, leveraging the moment-matching approach. Furthermore, we present closed-form asymptotic expressions for these performance metrics. The derived expressions are then leveraged to gain important insights into the impact of phase errors on the scaling laws of diversity order, coding gain, and ER. The numerical results show that the presence of phase errors can significantly degrade the system’s diversity order and coding gain. Specifically, we show that, in the absence of phase errors, the diversity order scales linearly with the number of RIS elements, while the diversity order reduces to unity in the presence of uniformly distributed phase errors. Finally, the analytical findings are demonstrated through extensive simulation results.

In integrated sensing and communication (ISAC) systems, reconfigurable intelligent surface (RIS) is often able to provide a virtual line-of-sight path to overcome blockage and introduce new degrees of freedom to enhance both communications and sensing performances. In this paper, we explore the synergistic integration of massive multiple-input-multiple-output (MIMO) ISAC and RIS technologies. We first consider the uplink training phase and estimate the channels between the base station and communication users, by using the minimum mean square error method. Then, we derive the closed-form expressions for the downlink communications rate and the Cramér-Rao lower bound for the estimation of the azimuth and elevation angles associated with the sensing target. Extensive simulations verify that the derived analytical results match well with Monte Carlo simulation results. Furthermore, communication and sensing performances are significantly improved owing to the deployment of the RIS. Our analysis lay an important foundation for optimizing RIS-assisted ISAC systems.

Unmanned aerial vehicle (UAV) and active reconfigurable intelligent surface (ARIS) can enhance wireless communications channels through flexible location deployment and intelligent signal reflection. On the other hand, rate-splitting multiple access (RSMA) is an efficient framework for non-orthogonal transmission and multiple access. In this paper, we explore the synergistic advantages of integrating these technologies within a unified UAV-mounted ARIS-assisted communications system employing RSMA. Specifically, we analyze the system outage probability and sum capacity considering independent and non-identically distributed Nakagami-m channels, circularly uniformly distributed random RIS phase shifts, and imperfect successive interference cancellation. We first derive the exact cumulative distribution and probability density functions of the sum single and double Nakagami-m vectors via multivariate Fox’s H-function. These allow us to derive the closed-form expressions for the outage probability, asymptotic outage probability, and sum ergodic capacity of the system. The analytical findings are then verified via extensive numerical simulations, which highlight the superior performance of UAV-ARIS RSMA compared to conventional UAV-based systems.

Gaussian filtering is a commonly used nonlinear filtering method. This letter proposes an advanced Gaussian filtering method for handling cyber-attacked measurement data. It considers three general forms of measurement data irregularities due to the attack, including false data injection (FDI), time asynchronous measurements (TAM), and denial-of-service (DoS). The proposed method introduces a modified measurement model to incorporate the possibility of these irregularities occurring simultaneously. Subsequently, it re-derives the traditional Gaussian filtering for the modified measurement model, resulting in the proposed filtering method. The improved accuracy of the proposed method is validated for two simulation problems.