Dual-function radar andcommunication (DRC) system has been recently recognized as a promising approach to solve the spectrum scarcity problem. However, when the target exists within a crowded area where pathloss dominating, the performance of radar may be severely degraded. To tackle this issue, this article proposes for the first time the deployment of an intelligent reflecting surface (IRS) to help the DRC system to enhance the radar detection performance. The IRS can configure the environment around the radar by adaptively adjusting the phases of its reflecting units to strengthen the signal quality toward specific directions, mostly the target direction, and completely null-out transmissions in other directions, mostly the directions toward the communication system. Specifically, in this article, we investigate the joint optimization of the IRS passive phase-shift matrix (PSM) and precoding matrix of the radar-aided basestation for the DRC system. The optimization is carried-out through maximizing the signal-to-noise ratio (SNR) at the radar receiver under both sensing and communication constraints, which turns out to be a nonconvex problem. In order to circumvent this challenging problem, an alternation optimization approach is employed to decouple the optimization variables and split this intractable problem into two subproblems. However, it is still challenging to obtain the optimal PSM due to the high power of the objective function and the unit-modulus constraints. To solve this problem, a majorization–minimization algorithm is conceived to transform the nonconvex problem to an easy to solve quadratic constraint quadratic programming problem. Simulation resultsdemonstrate that the IRS can help improving the performance of the DRC system in terms of the received SNR, and the proposed algorithm shows fast convergence.

This paper studies the physical-layer security (PLS) improvement in wireless communication systems, where a hybrid reconfigurable intelligent surfaces (RISs) is deployed between the transmitter and legitimate receiver to enhance the communication security. To achieve a tradeoff between the secrecy capacity and power consumption, the element on-off mechanism is considered firstly to represent the connection relationship between the RIS elements and power amplifier. To address this non-convex problem, two-stage cyclic iterative technique is utilized to the considered optimization problem by iteratively optimizing the transmit beamformingreflection coeffcient and amplifcation coeffcient. Simulation results demonstrate that the proposed algorithm can effectively improve the performance gains compared with existing solutions.

It is critical to design efficient beamforming in reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems for enhancing spectrum utilization. However, conventional methods often have limitations, either incurring high computational complexity due to iterative algorithms or sacrificing performance when using heuristic methods. To simultaneously achieve both low complexity and high spectrum efficiency, lightweight structures are employed to develop an unsupervised learning-based beamforming design in this work. We tailor image-shaped channel samples and develop an ISAC beamforming neural network (IBF-Net) model. By leveraging unsupervised learning, the loss function incorporates key performance metrics like sensing and communication channel correlation and sensing channel gain, eliminating the need for labeling. Simulations show that the proposed method achieves competitive performance compared to the benchmarks and significantly reduces the computational complexity.

Energy efficiency (EE) is a challenging task in integrated sensing and communication (ISAC) systems, where high spectral efficiency and low energy consumption appear as conflicting requirements. Although passive reconfigurable intelligent surface (RIS) has emerged as a promising technology for enhancing the EE of the ISAC system, the multiplicative fading feature hinders its effectiveness. This paper proposes the use of active RIS with its amplification gains to assist the ISAC system for EE improvement. Specifically, we formulate an EE optimization problem in an active RIS-aided ISAC system under system power budgets, considering constraints on user communication quality of service and sensing signal-to-noise ratio (SNR). A novel alternating optimization algorithm is developed to address the highly non-convex problem by employing the generalized Rayleigh quotient optimization, semidefinite relaxation (SDR), and the majorization-minimization (MM) framework. Furthermore, to reduce computational complexity, we derive a semi-closed form for eigenvalue determination. Numerical results demonstrate the effectiveness of the proposed approach, showcasing significant improvements in EE compared to both passive RIS and spectrum efficiency optimization cases.

This paper addresses the issue of beampattern synthesis with flexible magnitude response and low sidelobe in a frequency diverse array radar. It is formulated as an optimization problem in terms of ℓ ₁ -norm minimization, which encourages the sparsity of array pattern. An ℓ ₁ iterative phase compensation (IPC) technique is employed to transform the nonconvex constraint to phase compensation form. Then, to further reduce the sidelobe level, a reweighted ℓ ₁ -IPC algorithm is devised, in which a sparsity-enhanced scheme is utilized to convert the ℓ1-norm to a new cost function that more closely resembles ℓ ₀ -norm. Numerical results are presented to demonstrate the effectiveness of the proposed approach in different scenarios.

In order to deploy minimum number of unmanned aerial vehicle (UAV)-mounted mobile base stations (MBSs) to service all given ground terminals, this paper proposes an MBS placement based on sparse recovery (MBS-PBSR) algorithm. By exploiting the sparsity inherent in the differences between any two dedicated MBSs, the problem of UAV-mounted MBS placement could be formulated as an ℓ0-norm constrained optimization problem, which is then be solved by the reweighted ℓ1-norm method. Subsequently, the resulted solutions to the MBS placement are adjusted by the iterative redundant circle deletion algorithm, eventually leading to the redundant MBSs removal as much as possible. Simulation results demonstrate that our proposed MBS-PBSR algorithm works well with affordable computational complexity, and is nearly optimum in the sense of the number of deployed UAV-mounted MBSs.