半盲流体天线系统：基于统计分析的端口选择
Semi-Blind Fluid Antenna System: Port Selection via Statistical Analysis
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中文总结 AI 辅助
该研究针对理想流体天线系统需完整信道状态信息的缺陷，提出无需预训练或深度学习的半盲流体天线系统，通过条件分布分析选最优端口，性能接近理想方案且测量需求更少。
中文摘要 AI 辅助
流体天线系统（Fluid Antenna System, FAS）具备位置可重构性。然而，实时FAS存在一个潜在缺陷：它需要在每个通信时隙获取所有FAS端口的完整信道状态信息（Channel State Information, CSI），这种方案被称为理想FAS。考虑到实现理想FAS的难度，我们提出了一种基于不完整CSI的FAS方案，称为半盲FAS。本文首先介绍了FAS的时空框架，在此基础上开发了所提出的半盲FAS。所提半盲FAS是轻量级的，计算效率高，可扩展到任意数量的端口和时隙，且无需预训练或深度学习结构即可运行。该方案有效利用不完整的历史CSI来估计期望时隙下所有FAS端口的条件分布，从而识别出用于信号接收的统计最优端口。总体而言，半盲FAS的核心思想是从统计角度通过条件分布分析选择最优端口，其中最优性根据关注的场景定义。受信息论熵的启发，我们进一步提出了剩余熵功率比，以表征物理参数如何影响半盲FAS与理想FAS之间的性能差距。我们的分析表明，估计性能不仅取决于采样端口和时隙的数量，还取决于每个时隙给定CSI的端口的具体索引，即端口采样策略。这一关键因素在现有的端口估计研究中已被很大程度上忽略。数值结果表明，所提半盲FAS的性能可与理想FAS相媲美，在某些情况下甚至无法区分，同时所需的端口CSI测量数量显著更少，且端口切换速度更低。
英文摘要
The fluid antenna system (FAS) enables position reconfigurability. A potential drawback of real-time FAS, however, is that it requires complete channel state information (CSI) for each FAS port at every communication time slot, an approach referred to as ideal-FAS. Recognizing the difficulties of achieving ideal-FAS, we propose a FAS scheme based on incomplete CSI, referred to as semi-blind FAS. This paper first introduces the spatial-temporal framework of FAS, upon which the proposed semi-blind FAS is developed. The proposed semi-blind FAS is lightweight and computationally efficient, scalable to an arbitrary number of ports and time slots, and operates without pre-training or deep learning structures. The scheme effectively exploits incomplete historical CSI to estimate the conditional distribution across all FAS ports at the desired time slot, thereby identifying the statistical optimal port for signal reception. Generally, the key idea of semi-blind FAS is to select the optimal port through conditional distribution analysis, from a statistical perspective, with optimality defined according to the scenario of interest. Inspired by information-theoretic entropy, we further develop the residual entropy power ratio to characterize how physical parameters influence the performance gap between semi-blind FAS and ideal-FAS. Our analysis reveals that estimation performance depends not only on the number of sampled ports and time slots, but also on the specific indices of ports with given CSI at each time slot, i.e., the port sampling strategy. This critical factor has been largely overlooked in existing port estimation studies. Numerical results demonstrate that the proposed semi-blind FAS achieves performance comparable to, and in some cases indistinguishable from, that of ideal-FAS, while requiring significantly fewer port CSI measurements and lower port switching speeds.
