Stacked intelligent metasurfaces (SIMs) have recently emerged as an effective solution for next-generation wireless networks. A SIM comprises multiple metasurface layers that enable signal processing directly in the wave domain. Moreover, recent advances in flexible metamaterials have highlighted the potential of flexible intelligent metasurfaces (FIMs), which can be physically morphed to enhance communication performance. In this paper, we propose a stacked flexible intelligent metasurface (SFIM)-based communication system for the first time, where each metasurface layer is deformable to improve the system's performance. We first present the system model, including the transmit and receive signal models as well as the channel model, and then formulate an optimization problem to maximize the system sum rate under constraints on the transmit power budget, morphing distance, and the unit-modulus condition of the meta-atom responses. To solve this problem, we develop an alternating optimization framework based on the gradient projection method. Simulation results demonstrate that the proposed SFIM-based system achieves significant performance gains compared to its rigid SIM counterpart.

Integrated sensing and communication (ISAC) has emerged as a promising technology for sixth-generation (6G) communication networks. At the same time, ensuring the privacy of targets in ISAC is important in contexts where a malicious sensor is present. In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted ISAC system designed to protect a sensing region against an adversarial detector (AD), where the base station (BS) has imperfect knowledge of the AD’s location. The RIS consists of both reflecting and absorptive elements (the latter serving as sensing elements), which can be adaptively reconfigured to meet system requirements. Specifically, the system is designed to maximize the jamming power from the BS to the AD by jointly optimizing the transmit beamformer at the BS, the RIS phase-shift matrix, the receive beamformer at the RIS, and the allocation between reflecting and absorptive elements at the RIS while ensuring a minimum sensing signal-to-interference-plus-noise ratio (SINR) at sample points within the sensing region, as well as a minimum communication SINR for each user. To address this challenging optimization problem, we propose an alternating optimization framework combined with a successive convex approximation method tailored for each subproblem. Our results show that the proposed system model offers significant protection of the sensing area compared to the case where the target privacy is not considered. Simulations also confirm that the proposed adaptive RIS partitioning outperforms the fixed RIS partitioning approach.

Active reconfigurable intelligent surfaces (RISs) can improve the performance of integrated sensing and communication (ISAC), and therefore enable simultaneous data transmission and target sensing. However, when the line-of-sight (LoS) link between the base station and the sensing target is blocked, the sensing signals suffer from severe path loss, resulting in an inferior sensing performance. To address this issue, this paper employs a sensor-aided active RIS to enhance ISAC system performance. The goal is to maximize the signal-to-noise ratio of the echo signal from the target at the sensor-array while meeting constraints on communication signal quality, power budgets, and RIS amplification limits. The optimization problem is challenging due to its non-convex nature and the coupling between the optimization variables. We propose a closed-form solution for receive beamforming, and a successive convex approximation based iterative method for transmit and reflection beamforming design. Simulation results demonstrate the advantage of the proposed sensor-aided active RIS-assisted system model over its non-sensor-aided counterpart.

Users in millimeter-wave (mmWave) systems often exhibit diverse channel strengths, which can negatively impact user fairness in resource allocation. Moreover, exact channel state information (CSI) may not be available at the transmitter, rendering suboptimal resource allocation. In this paper, we address these issues within the context of energy efficiency maximization in reconfigurable intelligent surface (RIS)-assisted mmWave systems. We first derive a tractable lower bound on the achievable sum rate, taking into account CSI errors. Subsequently, we formulate the optimization problem, targeting maximizing the system energy efficiency while maintaining a minimum Jain’s fairness index controlled by a tunable design parameter. The optimization problem is very challenging due to the coupling of the optimization variables in the objective function and the fairness constraint, as well as the existence of non-convex equality and fractional constraints. To solve this optimization problem, we employ the penalty dual decomposition method, together with a projected gradient ascent based alternating optimization procedure. The proposed algorithm exhibits linear time complexity with respect to the number of RIS elements. Simulation results demonstrate that the proposed algorithm can achieve an optimal energy efficiency for a prescribed Jain’s fairness index. In addition, adjusting the fairness design parameter can yield a favorable trade-off between energy efficiency and user fairness compared to methods that exclusively focus on optimizing one of these metrics.

Reconfigurable intelligent surfaces (RISs) are considered to be a promising solution to overcome the blockage issue in the millimeter-wave (mmWave) band. Energy efficiency is an important performance metric in RIS-assisted mmWave systems with a large number of antennas. However, due to the severe path loss in mmWave systems, resource allocation algorithms tend to allocate most of the resources for the benefit of the users with higher channel gains. In this paper, we propose a lexicographic-based approach to find the optimal power allocation, RIS passive beamforming matrix, and analog precoders that maximize both energy efficiency and user fairness. We solve the corresponding multi-objective optimization problem in two stages. In the first stage, we maximize the energy efficiency, and in the second stage we maximize the fairness subject to a minimum energy efficiency constraint. We propose an alternating optimization procedure to solve the optimization problem in each stage. The optimal power allocation is found using Dinkelbach's method and convex optimization techniques in the first and second stage respectively, the RIS phase shift matrix is found using a gradient ascent algorithm, and the analog precoder is determined using beam alignment. Numerical results show that the proposed algorithm can achieve an excellent trade-off between the energy efficiency and fairness by boosting the minimum weighted rate with a minor and controllable reduction in the energy efficiency.