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Electromagnetic wave absorbing material (EWAM) plays an essential role in manufacturing stealth aircraft, which can achieve the electromagnetic stealth (ES) by reducing the strength of the signal reflected back to the radar system. However, the stealth performance is limited by the coating thickness, incident wave angles, and working frequencies. To tackle these limitations, we propose a new intelligent reflecting surface (IRS)-aided ES system where an IRS is deployed at the target to synergize with EWAM for effectively mitigating the echo signal and thus reducing the radar detection probability. Considering the monotonic relationship between the detection probability and the received signal-to-noise-ratio (SNR) at the radar, we formulate an optimization problem that minimizes the SNR under the reflection constraint of each IRS element, and a semi-closed-form solution is derived by using Karush-Kuhn-Tucker (KKT) conditions. Simulation results validate the superiority of the proposed IRS-aided ES system compared to various benchmarks. 

Compared with traditional half-duplex wireless systems, the application of emerging full-duplex (FD) technology can potentially double the system capacity theoretically. However, conventional techniques for suppressing self-interference (SI) adopted in FD systems require exceedingly high power consumption and expensive hardware. In this paper, we consider employing an intelligent reflecting surface (IRS) in the proximity of an FD base station (BS) to mitigate SI for simultaneously receiving data from uplink users and transmitting information to downlink users. The objective considered is to maximize the system weighted sum-rate by jointly optimizing the IRS phase shifts, the BS transmit beamformers, and the transmit power of the uplink users. To visualize the role of the IRS in SI cancellation, we first study a simple scenario with one downlink user and one uplink user. To address the formulated non-convex problem, a low-complexity algorithm based on successive convex approximation is proposed. For the more general case considering multiple downlink and uplink users, an efficient alternating optimization algorithm based on element-wise optimization is proposed. Numerical results demonstrate that the FD system with the proposed schemes can achieve a larger gain over the half-duplex system, and the IRS is able to achieve a balance between suppressing SI and providing beamforming gain. 

This paper explores the use of reconfigurable intelligent surfaces (RIS) in mitigating cross-system interference in spectrum sharing and secure wireless applications. Unlike conventional RIS that can only adjust the phase of the incoming signal and essentially reflect all impinging energy, or active RIS, which also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, an absorptive RIS (ARIS) is considered. An ARIS can in principle modify both the phase and modulus of the impinging signal by absorbing a portion of the signal energy, providing a compromise between its conventional and active counterparts in terms of complexity, power consumption, and degrees of freedom (DoFs). We first use a toy example to illustrate the benefit of ARIS, and then we consider three applications: 1) spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver; 2) spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that maximizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is developed and then solved via fractional programming; 3) physical layer security of a downlink communication system, where the secrecy rate is maximized and the resulting nonconvex problem is solved by a fractional programming algorithm together with a sequential convex relaxation procedure. Numerical results are then presented to show the significant benefit of ARIS in these applications. 

An innovative method has developed recently for biasing the varactors of a reconfigurable intelligent surface (RIS) by utilizing resonant standing waves on the “biasing transmission line (TL)” [E. Ayanoglu, F. Capolino, and A. L. Swindlehurst, “Wave-controlled metasurface-based reconfigurable intelligent surfaces,” IEEE Wireless Communications, vol. 29, no. 4, pp. 86-92,2022] located beneath the reflective surface. Using this approach, each RIS element does not require separate external biasing. For estimating the RIS reflection properties controlled by varactors, we analyze a planar array with phase gradient in one direction, of side length L, of reconfigurable elements. We employ the analytical model for predicting the reflection coefficients of the unit cells presented in [D. Hanna, M. Saavedra-Melo, F. Shan, and F. Capolino, “A versatile polynomial model for reflection by a reflective intelligent surface with varactors,” IEEE AP-S/URSI, 2022] and investigate how the standing wave biasing approach compares with the traditional way to generate field patterns of the reflected wave. 

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the potential target location of interest at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the potential target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the potential target location is uncertain in a region of interest, a robust algorithm based on the $$\cal S$$ -procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality, and also show the tremendous potential of IRS for use in sensing and improving the security of ISAC systems. 

The problem of optimizing discrete phases in a reconfigurable intelligent surface (RIS) to maximize the received power at a user equipment is addressed. Necessary and sufficient conditions to achieve this maximization are given. These conditions are employed in an algorithm to achieve the maximization. New versions of the algorithm are given that are proven to achieve convergence in N or fewer steps whether the direct link is completely blocked or not, where N is the number of the RIS elements, whereas previously published results achieve this in KN or 2N number of steps where K is the number of discrete phases. Thus, for a discrete-phase RIS, the techniques presented in this paper achieve the optimum received power in the smallest number of steps published in the literature. In addition, in each of those N steps, the techniques presented in this paper determine only one or a small number of phase shifts with a simple elementwise update rule, which result in a substantial reduction of computation time, as compared to the algorithms in the literature. As a secondary result, we define the uniform polar quantization (UPQ) algorithm which is an intuitive quantization algorithm that can approximate the continuous solution with an approximation ratio of sinc2(1/K) and achieve low time-complexity, given perfect knowledge of the channel. 

This paper explores the use of reconfigurable intelligent surfaces (RISs) for moving target detection in multi-input multi-output (MIMO) radar. Unlike previous related works that ignore the propa-gation delay difference between the direct path and the RIS-reflected path, we examine the detection problem in RIS-assisted MIMO radar by taking into account the effect of asynchronous propagation. Specifically, we first develop a general signal model for RIS-aided MIMO radar with multiple asynchronous RISs and arbitrary wave-forms. Next, we formulate the RIS design problem by maximizing the overall received signal energy. The resulting optimization problem is non-convex, which is solved with semidefinite relaxation (SDR) techniques. A coherent detector is introduced for target detection. Finally, numerical results are presented to demonstrate the performance of the RIS-aided MIMO radar in comparison with the conventional MIMO radar. 

This paper presents a novel reconfigurable intel-ligent surface (RIS)-based localization approach for mobile user equipment (UE) in a millimeter-wave uplink cellular environment. The proposed approach develops a measurement engine that employs a state-of-the-art carrier-aided code-phase-based navigation receiver and incorporates a passive correlation-based angle-locked loop (ALL) for TOA and AOA estimation. An extended Kalman filter (EKF)-based RIS-aided navigation framework is deployed, providing accurate 3D position and velocity estimates for the mobile UEs utilizing the RIS-based navigation observables, which are then leveraged to optimize the RIS phase profile to maximize the signal-to-noise ratio (SNR) for the various UEs. Finally, the paper demonstrates the accuracy of the navigation solution through extensive Monte Carlo simu-lations that encompass different scenarios involving pedestrians, ground vehicles, and unmanned aerial vehicles (UAVs), These simulations emphasize the utility of our proposed approach in delivering sub-meter and meter-level posltioning accuracies.