灵活智能超表面辅助的信息感知与通信一体化：用户公平性优化与性能评估
Flexible Intelligent Metasurface-Aided ISAC: User Fairness Optimization and Performance Evaluation
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中文总结 AI 辅助
研究灵活智能超表面与NOMA辅助的ISAC系统的最大最小用户公平性优化，推导目标感知中AoD估计的CRLB并嵌入优化框架，用交替优化算法解决非凸问题，提升用户公平性，平衡通信与感知性能，揭示相关耦合特性，为6G网络提供优化方案。
中文摘要 AI 辅助
本文研究了具有主动自定位功能的灵活智能超表面（FIM）和非正交多址接入（NOMA）辅助的信息感知与通信一体化（ISAC）系统的最大最小用户公平性优化。为解决传统ISAC设计中遇到的多用户干扰、性能不平衡和感知精度被忽视的问题，我们推导了目标感知中出发角（AoD）估计的闭式克拉美罗下界（CRLB），并将其嵌入到最大最小公平性优化框架中。通过交替优化（AO）算法解决了联合设计基站发射波束成形、FIM反射系数和表面变形的非凸优化问题。仿真结果验证了该方案显著提高了用户公平性，有效平衡了通信性能和感知精度，并揭示了信号与干扰加噪声比（SINR）和感知CRLB之间的耦合特性。这项工作为6G网络中FIM辅助的ISAC系统优化提供了可行的解决方案。
英文摘要
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.
