Secure High-Resolution ISAC via Multi-Layer Intelligent Metasurfaces: A Layered Optimization Framework

TL;DR

A multi-functional SIM-assisted system that jointly optimizes communication secrecy and sensing accuracy through a novel layered optimization framework is proposed, establishing a new paradigm for secure and high-precision multi-functional wireless systems.

Abstract

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless networks, enabling simultaneous data transmission and environmental sensing. However, existing ISAC systems face fundamental limitations in achieving high-resolution sensing while maintaining robust communication security and spectral efficiency. This paper introduces a transformative approach leveraging stacked intelligent metasurfaces (SIM) to overcome these challenges. We propose a multi-functional SIM-assisted system that jointly optimizes communication secrecy and sensing accuracy through a novel layered optimization framework. Our solution employs a multi-objective optimization formulation that balances secrecy rate maximization with sensing error minimization under practical hardware constraints. The proposed layered block coordinate descent algorithm efficiently coordinates sensing configuration, secure beamforming, communication metasurface optimization, and resource allocation while ensuring robustness to channel uncertainties. Extensive simulations demonstrate significant performance gains over conventional approaches, achieving 32-61\% improvement in sensing accuracy and 15-35\% enhancement in secrecy rates while maintaining computational efficiency. This work establishes a new paradigm for secure and high-precision multi-functional wireless systems.

Figures (9)

• Figure 1: Weighted utility versus BS transmit power $P_{\mathrm{BS}}^{\max}$ for $\alpha=0.5$, comparing L-BCD with TS, CF, SF, SL-RIS, and NR-LBCD.
• Figure 2: Sum secrecy rate, inverse normalized CRB, and scalarized utility versus trade-off parameter $\alpha$, illustrating the communication–sensing Pareto frontier.
• Figure 3: Outage secrecy rate versus channel-uncertainty bound $\epsilon_h^{\max}$ for robust L-BCD, NR-LBCD, TS, and the analytical robustness bound.
• Figure 4: Relative secrecy and sensing performance gain of an $L$-layer SIM over a single-layer RIS as a function of the number of layers $L$.
• Figure 5: Normalized objective gap of L-BCD versus iteration index, compared with $\mathcal{O}(1/t)$ and $\mathcal{O}(1/\sqrt{t})$ reference decay curves.

Theorems & Definitions (22)

• Theorem 1: Global Convergence of Enhanced 5-Block L-BCD
• proof
• Proposition 1: Constraint Qualification
• proof
• Corollary 1: Convergence Rate for KL Framework
• proof
• Lemma 1: Initialization Quality Impact
• proof
• Theorem 2: Tighter Secrecy-Sensing Trade-off
• proof