Reconfigurable intelligent surface (RIS) is a promising technology for 6 G networks owing to its superior ability to enhance the capacity and coverage of wireless communications by smartly creating a favorable propagation environment. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) systems for improving both radar sensing and communication functionalities. In particular, we consider a RIS-assisted DFRC system in which the multi-antenna base station (BS) simultaneously performs both multi-input multi-output (MIMO) radar sensing and multi-user multi-input single-output (MU-MISO) communications using the same hardware platform. We aim to jointly design the dual-functional transmit waveform and the passive beamforming of RIS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) achieved by space-time adaptive processing (STAP), while satisfying the communication quality-of-service (QoS) requirement under one of three metrics, the constant-modulus constraint on the transmit waveform, and the unit-modulus constraint of RIS reflecting coefficients. An efficient algorithm framework based on the alternative direction method of multipliers (ADMM) and majorization-minimization (MM) methods is developed to solve the complicated non-convex optimization problem. Simulation results verify the advancement of the proposed RIS-assisted DRFC scheme and the effectiveness of the developed ADMM-MM-based joint transmit waveform and passive beamforming design algorithm.

In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramér-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. We then extend the proposed joint design algorithms to the scenario with imperfect self-interference cancellation. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for direct-of-arrival (DoA) estimation than for target detection.

Integrated sensing and communication (ISAC) has been envisioned as a promising technology to tackle the spectrum congestion problem for future networks. In this correspondence, we investigate to deploy a reconfigurable intelligent surface (RIS) in an ISAC system for achieving better performance. In particular, a multi-antenna base station (BS) simultaneously serves multiple single-antenna users with the assistance of a RIS and detects potential targets. The active beamforming of the BS and the passive beamforming of the RIS are jointly optimized to maximize the achievable sum-rate of the communication users while satisfying the constraint of beampattern similarity for radar sensing, the restriction of the RIS, and the transmit power budget. An efficient alternating algorithm based on the fractional programming (FP), majorization-minimization (MM), and manifold optimization methods is developed to convert the resulting non-convex optimization problem into two solvable sub-problems and iteratively solve them. Simulation studies illustrate the advancement of deploying RIS in ISAC systems and the effectiveness of the proposed algorithm.

Dual-functional radar-communication (DFRC) systems can simultaneously perform both radar and communication functionalities using the same hardware platform and spectrum resource. In this paper, we consider multi-input multi-output (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multi-user communications. Unlike conventional block-level precoding techniques, we propose to use the recently emerged symbol-level precoding approach in DFRC systems, which provides additional degrees of freedom (DoFs) that guarantee preferable instantaneous transmit beampatterns for radar sensing and achieve better communication performance. In particular, the squared error between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of the communication users and the constant-modulus power constraint. Two efficient algorithms are developed to solve this non-convex problem on both the Euclidean and Riemannian spaces. The first algorithm employs penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the original optimization problem into two solvable sub-problems, and iteratively solves them using efficient algorithms. The second algorithm provides a much faster solution at the price of a slight performance loss, first transforming the original problem into Riemannian space, and then utilizing the augmented Lagrangian method (ALM) to obtain an unconstrained problem that is subsequently solved via a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm. Extensive simulations verify the distinct advantages of the proposed symbol-level precoding designs in both radar sensing and multi-user communications.

Integrated sensing and communication (ISAC) is recognized as a promising technology with great potential in saving hardware and spectrum resources, since it simultaneously realizes radar detection and user communication functions in the fully-shared platform. Employing reconfigurable intelligent surface (RIS) in ISAC systems is able to provide a virtual line-of-sight (LoS) path to conquer blockage problem as well as introduce new degrees of freedom (DoFs) to further enhance system performance. Nevertheless, the multiplicative fading effect of passive RIS limits its applications in the absence of direct links, which promotes the development of active RIS. In this paper, we consider an active RIS-assisted ISAC system and aim to jointly design the transmit beamformer, the active RIS reflection and the radar receive filter to maximize the radar output signal-to-noise ratio (SNR) while guaranteeing pre-defined signal-to-interference-plus-noise ratios (SINRs) for communication users. To solve for this non-convex problem, an efficient algorithm is developed by leveraging the techniques of block coordinate descent (BCD), Dinkelbach's transform and majorization-minimization (MM). Simulation results verify the significant advancement of deploying active RIS in ISAC systems, which can achieve up to 32 dB radar SNR enhancement compared with the passive RIS-assisted ISAC systems.

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.