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.
The convergence of communication and sensing functionalities represents one of the most significant paradigm shifts in beyond-5G and 6G wireless networks [1], [2]. Integrated sensing and communication (ISAC) has garnered substantial research interest due to its potential to enable diverse applications including autonomous vehicles [3], smart cities [4], and industrial IoT [10]. The fundamental premise of ISAC lies in the joint utilization of spectral and hardware resources for dual purposes, thereby improving spectral efficiency and reducing infrastructure costs [5], [6].
Recent advances in ISAC architectures have demonstrated promising performance, yet several critical challenges remain unaddressed. Conventional ISAC systems [8], [26] often face inherent trade-offs between sensing resolution and communication throughput, particularly in multi-user scenarios. The work Amirhossein Taherpour is with the Department of Electrical Engineering, Columbia University, New York, NY, USA (e-mail: at3532@columbia.edu) Abbas Taherpour is with the Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran (e-mail: taherpour@ikiu.ac.ir).
and Tamer Khattab is with the Department of Electrical Engineering, Qatar University, Doha, Qatar (e-mail: tkhattab@ieee.org).
in [32] highlighted the limitations of time-sharing approaches, while [30] demonstrated the vulnerability of ISAC systems to security threats. Physical layer security has emerged as a crucial consideration, with [7], [12] showing that eavesdroppers can exploit sensing signals to compromise communication confidentiality.
The integration of reconfigurable intelligent surfaces (RIS) has opened new avenues for enhancing ISAC performance [11], [16]. RIS-assisted ISAC systems [14], [13] have demonstrated improved coverage and beamforming gains. However, single-layer RIS architectures face fundamental limitations in wave manipulation capabilities [17]. Recent breakthroughs in stacked intelligent metasurfaces (SIM) [18], [19] have revealed unprecedented opportunities for advanced wave-based signal processing. Unlike conventional RIS, multi-layer SIM can perform volumetric analog computing, enabling sophisticated spatial filtering and beamforming [20].
Security considerations in ISAC systems have gained increasing attention, with [21] proposing artificial-noise-based approaches and [22] developing optimization frameworks for secrecy rate maximization. However, these methods often neglect the interplay between sensing accuracy and communication security. The work in [23] identified fundamental performance trade-offs, while [34] proposed resource allocation strategies for balancing these competing objectives.
Channel uncertainty poses another significant challenge for practical ISAC deployment. Robust optimization techniques have been explored in [26] and [25], but existing approaches typically assume simplified uncertainty models. The recent work in [30] introduced probabilistic constraints for outage performance, while [28] addressed the impact of finite-resolution phase shifters on system performance.
Multi-functional metasurface architectures represent a promising direction for overcoming current limitations. The pioneering work in [29] demonstrated dynamic beamforming capabilities, and [25] achieved simultaneous multi-target sensing and channel estimation in RIS-assisted systems. However, these approaches lack comprehensive optimization frameworks that jointly address communication security, sensing accuracy, and resource efficiency. The survey in [13] identified the need for integrated optimization methodologies as a critical research gap.
Recent developments in optimization theory have provided new tools for addressing ISAC challenges. Block coordinate descent and distributed methods [35] have shown promise for complex large-scale problems, while multi-objective and successive convex approximation techniques [34], [31], [33] enable efficient handling of intricate constraints. Despite these advances, several fundamental questions remain unanswered. How can we achieve simultaneous high-precision sensing and secure communication in practical scenarios with hardware constraints? What are the fundamental performance limits of SIM-assisted ISAC systems? How can we efficiently optimize multi-layer metasurface configurations while ensuring robustness against channel uncertainties?
This paper addresses these critical questions through the following contributions:
• We introduce a novel multi-functional SIM-assisted ISAC architecture that enables joint communication and sensing with enhanced security and accuracy. • We develop a comprehensive multi-objective optimization framework that simultaneously maximizes secrecy rates and minimizes sensing errors under practical constraints. • We propose an efficient layered block coordinate descent algorithm that coordinates sensing configuration, secure beamforming, communication metasurface opt
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