Hybrid RIS-Enhanced ISAC Secure Systems: Joint Optimization in the Presence of an Extended Target

TL;DR

This paper introduces a joint optimization framework for hybrid RIS-enhanced ISAC systems that improves secure communication and extended target detection by leveraging active and passive RIS units with advanced optimization techniques.

Contribution

It proposes a novel joint design of signals, beamformers, and RIS weights for hybrid RIS-assisted ISAC systems considering imperfect target location knowledge.

Findings

Enhanced extended target detection performance.

Improved secure transmission compared to existing RIS approaches.

Effective optimization of system variables using GFP and PDD methods.

Abstract

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known target location (TL), we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the weights of the hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.