Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems

Abstract

Integrated sensing and communications (ISAC) is emerging as a critical technique for next-generation communication systems. Reconfigurable intelligent surface (RIS) can simultaneously enhance the performance of communication and sensing by introducing new degrees-of-freedom for beamforming in ISAC systems. This paper proposes two optimization techniques for joint beamforming in RIS-assisted ISAC systems. We first aim to maximize the radar mutual information (MI) by imposing constraints on communication rate, transmit power, and unit modulus reflection coefficients at the RIS. An alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method is proposed to solve the optimization problem by introducing a convergence-accelerating method. To achieve lower computational complexity and better reliability, we then formulate a new optimization problem for maximizing the weighted ISAC performance metrics under fewer constraints. An AO algorithm based on the Riemannian gradient (RG) method is proposed to solve this problem. This is achieved by reformulating the transmit and RIS beamforming on the complex hypersphere manifold and complex circle manifold, respectively.Numerical results show that the proposed algorithms can enhance the radar MI and the weighted communication rate simultaneously. The AO algorithm based on RG exhibits better performance than the SDR-based method. I. INTRODUCTION Many emerging applications for future networks, such as vehicle-to-everything and smart homes, require high-quality communication performance and demand accurate sensing capabilities [1], [2]. The communication and sensing functionality can be simultaneously realized in a unified platform due to their similar hardware architecture and signal processing algorithms. Y. Xu and Y. Li are with the

Digital cultivation and planting system based on Internet of Things

“…Refs. [13]- [17] delved into the active and passive beamforming design of RIS-aided ISAC systems, each with unique design criteria ranging from weighted radar SNR summation maximization, user SNR maximization, weighted combination of the radar mutual information (MI) and the communication sum-rate maximization, radar output signal-to-interference-plus-noise ratio (SINR) maximization and the radar MI maximization.…”