A fluid reconfigurable intelligent surface (fRIS)-aided integrated sensing and communication (ISAC) system is proposed to enhance multi-target sensing and multi-user communication. Unlike the conventional RIS, the fRIS employs movable elements with adjustable positions, offering additional spatial degrees of freedom. In this system, a joint optimization problem is formulated to minimize sensing beampattern mismatch and symbol estimation error. An algorithm based on alternating minimization is devised to handle the resultant non-convex problem, where the subproblems are solved via augmented Lagrangian method, quadratic programming, semidefinite relaxation, and majorization-minimization. A key challenge is that the element positions affect both incident and reflective channels, leading to the high-order composite objective functions. As a remedy, the high-order terms are transformed into linear and linear-difference forms by exploiting the structural characteristics of fRIS and the channels. Numerical results demonstrate the superiority of the proposed scheme over conventional RIS-aided ISAC and other benchmarks.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have emerged as promising technologies for enhancing wireless coverage and connectivity due to their mobility and reconfigurability. In this paper, we investigate a multi-user system assisted by multiple UAV-mounted RISs, where each UAV carries a RIS to enhance the communication link between the base station (BS) and users. We aim to maximize the system sum-rate by jointly optimizing the BS beamforming, the phase shifts of RISs, and the location of UAVs. To address this problem, we develop an alternating optimization algorithm based on the fractional programming framework. The original problem is decomposed into three subproblems to obtain the optimal BS beamforming, RIS phase shift, and the UAV location. Numerical results demonstrate that the proposed algorithm effectively achieves the sum-rate performance.

Low-cost and low-power consumptions have become the trend for the future communication systems, where one-bit quantization is a promising candidate. However, due to the low resolutions, the performance loss of the one-bit system is serious. To alleviate this issue, we propose leveraging the fluid reconfigurable intelligent surface (fRIS) to compensate for the loss in downlink communication systems with one-bit digital-to-analog converters (DACs). Specifically, we formulate a symbol estimation error minimization problem by jointly optimizing the symbol estimator, the one-bit transmit signal, the fRIS phase shifts, and the element positions. To handle the resultant problem, an alternating algorithm is developed, where four subproblems are solved iteratively by using semidefinite-relaxation, discrete optimization, and quadratic programming. In optimizing fRIS element positions, the positions affect both the incident and reflective channels, leading to the high-order complex objective functions. We show that these terms can be simplified by using the characteristics of channels. Numerical results validate that the introduction of fRIS can alleviate the performance loss caused by one-bit quantization.

Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive energy-hungry radio frequency (RF) chains hinder its large-scale deployment. To address this issue, an A-RIS-aided ISAC system with antenna selection (AS) is proposed in this work, where a target is sensed while multiple communication users are served with specifically selected antennas. Specifically, a cuckoo search-based scheme is first utilized to select the antennas associated with high-gain channels. Subsequently, with the properly selected antennas, the weighted sum-rate (WSR) of the system is optimized under the condition of radar probing power level, power budget for the A-RIS and transmitter. To solve the highly non-convex optimization problem, we develop an efficient algorithm based on weighted minimum mean square error (WMMSE) and fractional programming (FP). Simulation results show that the proposed AS scheme and the algorithm are effective, which reduces the number of RF chains without significant performance degradation.

Integrated dual functional radar and communication (DFRC) has been regarded as one of the most promising technologies. However, beamforming design in DFRC system is a challenge due to its nonconvexity. Inspired by the studies of active reconfigurable intelligent surface (RIS), we propose a joint transmitter and receiver design with adaptive beamforming for an active RIS aided DFRC system, where the active RIS is utilized to assist the DFRC to simultaneously detect targets and serve multiple users. Specifically, the WMMSE design criterion is exploited to design the transmit waveform of the DFRC by maximizing the sum-rate (SR) of users. Then, the Minorize-Maximization (MM) framework is derived to jointly optimize the amplification and phase shift coefficients of the active RIS. Simulation results show that when the active RIS is introduced, the SR of the system will improve greatly as compared to the scheme with passive RIS.