Reconfigurable Intelligent Surfaces (RIS) have been recognized as a promising technology to enhance both communication and sensing performance in integrated sensing and communication (ISAC) systems for future 6G networks. However, existing RIS optimization methods for improving ISAC performance are mainly based on semidefinite relaxation (SDR) or iterative algorithms. The former suffers from high computational complexity and limited scalability, especially when the number of RIS elements becomes large, while the latter yields suboptimal solutions whose performance depends on initialization. In this work, we introduce a lightweight RIS phase design framework that provides a closed-form solution and explicitly accounts for the trade-off between communication and sensing, as well as proportional beam gain distribution toward multiple sensing targets. The key idea is to partition the RIS configuration into two parts: the first part is designed to maximize the communication performance, while the second introduces small perturbations to generate multiple beams for multi-target sensing. Simulation results validate the effectiveness of the proposed approach and demonstrate that it achieves performance comparable to SDR but with significantly lower computational complexity.

Reconfigurable intelligent surfaces (RIS) are a technology foreseen to play an important role in sixth-generation (6G) wireless networks. By manipulating and reflecting electromagnetic waves using adjustable reflecting elements, RIS can actively reshape the wireless environment in real time. This capability proves most effective when the RIS is positioned in line-of-sight and proximity to the transmitter and receiver due to multiplicative path loss. To meet these criteria in real-world applications, the RIS is mounted on an unmanned aerial vehicle (UAV). Combining UAV with RIS is particularly advantageous due to the lightweight and energy-efficient nature of the RIS. However, a UAV is exposed to many disturbances during flight, which can compromise the performance benefits of the RIS-enabled link. We show the UAV and RIS orientation can be estimated with an extended Kalman filter (EKF). By conducting experimental measurements and recording the UAV-mounted RIS orientation during flight, we quantify the uncertainties regarding the orientation estimation of the UAV and show their impact on the performance of the RIS link through numerical simulations.

Joint communication and sensing (JCAS) systems aim to perform communication and radar duties simultaneously, through a single unified transmit waveform. They are expected to exploit mutually beneficial information between the JCAS functionalities through sharing hardware, spectrum and other network resources. In this paper, we consider a JCAS base station (BS) serving a single multi-antenna communication user with perfect channel state information (CSI), while simultaneously sensing an extended target without any prior knowledge. We formulate a beamforming optimisation problem to jointly max-imise the sum-rate at the communication user and the inverse Cramer-Rao bound (CRB) on the sensing target response matrix at the JCAS receiver under a total transmit power constraint. We are able to characterize the optimal beamforming direction in closed form, while the remaining scalar power allocation is solved numerically. We present a generalised low-complexity convex formulation of the JCAS beamforming optimisation and discuss it's validity for both, full rank and rank deficient beamformers. Numerical results indicate a performance gain of upto 37% in sum-rate at the same CRB with optimal antenna selection for the JCAS design compared to random selection. Further, the results illustrate JCAS functionality trade-offs and demonstrate flexibility in joint design for choosing the optimal CRB-rate pairs as a function of a CRB trade-off parameter.

Reconfigurable intelligent surfaces (RIS) are a core component considered in sixth generation (6G) communications. By utilizing an RIS prototype system in the 5 GHz band, this paper provides a comprehensive dataset of channel measurements of various geometric arrangements of antennas and RIS. The dataset and the in-detail documentation is provided on IEEE DataPort and GitHub, respectively. Since only a few datasets with measurements of RIS prototypes are currently available, we try to fill an important gap in current research by providing this systematically measured dataset. Due to the consistent quality of the measurements that is ensured by the measurement chamber, the provided dataset is also well suited to be used as a basis for machine learning concepts.In order to provide a solid baseline for comparing and validating a wide range of applications in different scenarios, the measurements are taken in stationary scenarios. This includes different RIS-to-antenna distances with specular reflection angles, non-specular reflection angles and with the RIS placed on a rotary stage. The selected switching states of the RIS for each scenario are determined by two iterative algorithms, namely a greedy algorithm and a single-element optimization approach, as well as multiple model-based approaches. The evaluation of the data in this paper compares the overall performance for the maximization and minimization of an RIS-assisted communication link, which shows an increase in received power of up to 20 dB compared to a reference plate.

Reconfigurable intelligent surfaces (RIS) are a technology expected to meet the demanding objectives of future wireless networks by manipulating and reflecting electromagnetic waves using real-time adjustable elements to improve communication performance. However, due to multiplicative path loss, RIS are most effective when used in line-of-sight and proximity to the transmitter or receiver. Moreover, as mobility is a common feature in wireless communication networks, guaranteeing these requirements can be challenging — unless the RIS has the ability to reposition itself.To investigate the efficacy of RIS for improving communication quality in such networks, we mounted a RIS prototype on an unmanned aerial vehicle (UAV). By conducting experimental measurements and recording the UAV-mounted RIS trajectory during its flights, the measured data was reconstructed through numerical simulations to extract the key factors. To further bridge simulations and measurements, we extract the gain characterizing the improvement in communication performance resulting from real-time adjustments of the RIS configuration, compared to a fixed configuration. Numerical evaluations indicate that a real-time adjusted RIS may result in a gain of up to 20dB.

The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, not only increases the signal quality but also the interference at the users. To counteract this negative side effect, we employ rate splitting (RS), which inherently is able to mitigate the impact of interference. We facilitate practical implementation by considering a Cloud Radio Access Network (C-RAN) at the cost of finite fronthaul-link capacities, which necessitate the allocation of sensible user-centric clusters to ensure energy-efficient transmissions. Dynamic methods for RS and the user clustering are proposed to account for the interdependencies of the individual techniques. Numerical results show that the dynamic RS method establishes synergistic benefits between RS and the IRS. Additionally, the dynamic user clustering and the IRS cooperate synergistically, reflected by increased individual gains for the dynamic clustering. Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases. Around the resulting intersection, both methods affect the system concurrently, improving the energy efficiency drastically.

The concept of intelligent reflecting surfaces (IRSs) is considered as a promising technology for increasing the efficiency of mobile wireless networks. This is achieved by employing a vast amount of low-cost individually adjustable passive reflect elements, that are able to apply changes to the reflected signal. To this end, the IRS makes the environment real-time controllable and can be adjusted to significantly increase the received signal quality at the users by passive beamsteering. However, the changes to the reflected signals have an effect on all users near the IRS, which makes it impossible to optimize the changes to positively influence every transmission affected by the reflections. This results in some users not only experiencing better signal quality, but also an increase in received interference. To mitigate this negative side effect of the IRS, this paper utilizes the rate splitting (RS) technique, which enables the mitigation of interference within the network in such a way that it also mitigates the increased interference caused by the IRS. To investigate the effects on the overall power savings, that can be achieved by combining both techniques, we minimize the required transmit power, needed to satisfy per-user quality-of-service (QoS) constraints. Numerical results show the improved power savings, that can be gained by utilizing the IRS and the RS technique simultaneously. In fact, the concurrent use of both techniques yields power savings, which are beyond the cumulative power savings of using each technique separately.