Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated.

In this paper, a novel unmanned aerial vehicle (UAV) secure communication system enhanced by the passive coupled phase-shift simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR- RIS) is investigated. Considering the multiple-user and multiple-eavesdropper systems, we aim to maximize the minimum average secrecy rate by the joint design of the UAV base station's (UAV-BS's) beamforming, the UAV's trajectory, and the STAR-RIS's transmission and reflection (T&R) coefficients. Since the formulated optimization problem is highly non-convex with intricately coupled variables, we decompose it into three subproblems, and resolve them alternately by semi-definite relaxation, successive convex approximation, and penalty-based approach. Simulation results show that: 1) the practical coupled phase-shift model of passive STAR-RIS suffers a security performance degradation compared with the ideal independent phase-shift model, but it still outperforms the conventional RIS; 2) the UAV's trajectory tends to be closer to the STAR-RIS to fully exploit the benefits of STAR-RIS; 3) the amplitudes of T&R coefficients highly depend on the UAV's real-time trajectory.

Recent years have witnessed an increasing interest in leveraging reconfigurable intelligent surfaces (RISs) to enhance the capabilities of integrated sensing and communication (ISAC) systems. RISs are advantageous in improving detection and communication performance, especially in challenging environments characterized by nonLine of Sight (NLOS) conditions and dense urban settings. In this article, a hybrid RIS, comprising passive reflecting elements and active sensors, and multiple fully passive RISs are deployed to enhance an ISAC system, where the direct paths between the base station (BS) and users/targets are blocked. The signal sent from the BS and reflected by RISs is received by the communication user, and simultaneously scattered by the target toward the sensors of the hybrid RIS. A joint optimization of the transmit covariance matrix at the BS and phase-shifting matrices at RISs is formulated, which considers the tradeoff between the communication and sensing performance. The optimization is based on the derived closed-form communication achievable rate by leveraging the free probability theory and positioning error bound (PEB) via the Cramér-Rao lower bound (CRLB) analysis. The block coordinate descent (BCD) algorithm is utilized to tackle the nonconvex problem, where the transmit covariance matrix and phase-shifting matrices are optimized iteratively. Therein, the Riemannian gradient descent algorithm is exploited for optimizing the phase-shifting matrices. Numerical results verify the effectiveness of the proposed algorithm, and both communication and sensing performance gains increase with the number of RIS panels and RIS elements.

Providing high-speed communication for mobile users in remote geographic areas or after a disaster occurs is not only critical but also challenging. To counter such challenge, unmanned aerial vehicles (UAVs) have been exploited to provide a fast-deployable and high-speed communication system, where each UAV can serve as an aerial small cell base station to provide WiFi and/or cellular services for the ground users. Despite its fast-deployable and highly maneuverable features, the capacity analysis of aerial small cells is largely missing. To close such gap, a stochastic propagation model for A-to-G aerial channels is first introduced, which takes into account the impact from wave propagation, gaseous absorption, Doppler spread, attitude-dependent shadowing, and multipath fading. Then, by exploiting such model, the area spectral efficiency of the aerial small cells is investigated for both SISO and MIMO cases. Our study reveals the inherent relationship among the area capacity, height and coverage and shows that there exists an optimal attitude that can maximize network capacity and cell coverage.