Cram\'er-Rao Bound Minimization for Flexible Intelligent Metasurface-Enabled ISAC Systems

2026-01-23

AI Summary

This work addresses integrated sensing and communication (ISAC) systems empowered by flexible intelligent metasurfaces (FIMs), focusing on jointly optimizing beamforming and the geometric shapes of transmit and receive FIM surfaces to minimize the Cramér–Rao bound (CRB)—the theoretical lower bound on sensing performance. It is the first to reveal how FIM geometric deformations influence the CRB, proposing instead to maximize the average Fisher information as a tractable surrogate objective. A decoupled cooperative optimization framework is developed, where the objective function is approximated via Gauss–Hermite quadrature, beamforming is optimized using Schur complement and penalty-based semidefinite relaxation, and the FIM shapes are updated via fixed-point equations and projected gradient methods. Simulations demonstrate that the proposed approach significantly reduces the average CRB, outperforms rigid arrays even in multi-target scenarios, and maintains robust communication performance.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Integrated sensing and communication (ISAC) have been widely recognized as a key enabler for future wireless networks, where the Cram\'er-Rao bound (CRB) plays a central role in quantifying sensing accuracy.In this paper, we present the first study on CRB minimization in flexible intelligent metasurface (FIM)-enabled ISAC systems.Specifically, we first derive an average CRB expression that explicitly depends on FIM surface shape and demonstrate that array reconfigurability can substantially reduce the CRB, thereby significantly enhancing sensing performance.Moreover, to tackle the challenging CRB minimization problem, we adopt average Fisher information maximization as a surrogate objective and use the Gauss-Hermite quadrature method to obtain an explicit approximation of the objective function.The resulting problem is then decoupled into three subproblem, i.e., beamforming optimization and transmit/receive FIM surface shape optimization.For beamforming optimization, we employ the Schur complement and penalty-based semi-definite relaxation (SDR) technique to solve it.Furthermore, we propose a fixed-point equation method and a projected gradient algorithm to optimize the surface shapes of the receive and transmit FIMs, respectively.Simulation results demonstrate that, compared to rigid arrays, surface shaping of both transmit and receive FIMs can significantly reduce the average sensing CRB while maintaining communication quality, and remains effective even in multi-target scenarios.

Problem

• Cramér-Rao Bound
• Intelligent Metasurface
• Integrated Sensing and Communication
• CRB Minimization
• Fisher Information

Innovation

• Cramér-Rao Bound
• Flexible Intelligent Metasurface
• Integrated Sensing and Communication
• Fisher Information Maximization
• Surface Shape Optimization