Flexible Intelligent Metasurface-Aided ISAC: User Fairness Optimization and Performance Evaluation

2026-07-21

AI Summary

This work addresses the challenges of multi-user interference, performance unfairness, and overlooked sensing accuracy in conventional integrated sensing and communication (ISAC) systems by proposing a 6G ISAC framework assisted by reconfigurable intelligent metasurfaces and non-orthogonal multiple access. For the first time, the Cramér–Rao lower bound (CRLB) for sensing is explicitly incorporated into a fairness-aware optimization framework, yielding a max-min fairness model constrained by angle estimation CRLB. By jointly optimizing base station beamforming, metasurface reflection coefficients, and physical deformation, the coupling between communication SINR and sensing CRLB is analytically revealed. Leveraging a closed-form CRLB derivation and a non-convex joint optimization formulation, an efficient alternating optimization algorithm is devised, which simultaneously enhances user fairness, communication performance, and sensing accuracy, thereby validating the feasibility of the proposed scheme in Fisher information matrix–aided ISAC systems.

Abstract

This paper investigates max-min user fairness optimization for flexible intelligent metasurface (FIM) and non-orthogonal multiple access (NOMA)-assisted integrated sensing and communication (ISAC) systems with active self-localization. To tackle the multi-user interference, performance imbalance, and neglected sensing accuracy problems encountered in conventional ISAC designs, we derive the closed-form Cramer-Rao lower bound (CRLB) for angle-of-departure (AoD) estimation in target sensing and embed it into a max-min fairness optimization framework. The optimization problem, which jointly designs the base station transmit beamforming, FIM reflection coefficients, and surface deformation, is non-convex and solved by an alternating optimization (AO) algorithm. Notably, the devised optimization framework facilitates superior performance trade-off in terms of spectrum resource utilization between communication and sensing tasks. Simulation results validate that the proposed scheme significantly improves user fairness, balances communication performance and sensing precision effectively, and reveals the coupling characteristic between signal-to-interference-plus-noise ratio (SINR) and sensing CRLB. This work provides a feasible solution for FIM-aided ISAC system optimization in 6G networks.

Problem

• ISAC
• user fairness
• multi-user interference
• sensing accuracy
• performance imbalance

Innovation

• Flexible Intelligent Metasurface
• Integrated Sensing and Communication
• Max-Min Fairness
• Cramer-Rao Lower Bound
• Alternating Optimization