Towards SAFE-ISAC: STAR-RIS-Aided Joint Jamming Suppression and Target Concealment

TL;DR

This paper introduces the SAFE-ISAC framework using STAR-RIS to jointly suppress jamming and conceal targets in wireless sensing and communication systems, optimizing reflection and transmission responses.

Contribution

It proposes a novel joint optimization approach for STAR-RIS to enhance jamming suppression and target concealment in ISAC networks, addressing a complex non-convex problem.

Findings

The scheme effectively mitigates jamming and conceals targets in simulations.

It balances communication and sensing QoS requirements.

Numerical results outperform existing benchmarks.

Abstract

Designing robust architectures that can mitigate sophisticated attacks is now a key priority for modern wireless systems. This paper investigates a single-cell bistatic integrated sensing and communication (ISAC) network facing simultaneous coordinated active jamming and malicious detection. These threats aim to disrupt the downlink communication and detect the presence of the ISAC target, respectively. To counter these attacks, we propose the SAFE-ISAC framework, which utilizes a simultaneous transmit and reflect reconfigurable intelligent surface (STAR-RIS) to jointly suppress jamming power and reduce the malicious detector's Signal-to-Interference-plus-Noise Ratio (SINR). We formulate a joint minimization problem for jamming gain and detection probability by optimizing the STAR-RIS reflection and transmission responses. This non-convex problem is decoupled into two subproblems: i) malicious detection mitigation in the transmission subspace, solved using the Dinkelbach method and Semidefinite Programming (SDP) relaxation, and ii) jamming suppression in the reflection subspace, addressed via Polak-Reib\'ere Riemannian conjugate gradient algorithm. Numerical results validate that the proposed scheme effectively achieves jamming mitigation and target concealment while meeting all communication and sensing Quality-of-Service (QoS) requirements, compared to existing benchmarks.