发表机构
University of Tehran; Iran University of Science and Technology; University at Buffalo, The State University of New York（德黑兰大学; 伊朗科技大学; 纽约州立大学布法罗分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对部分CSI下的流体天线系统，采用主动推理方法联合优化端口选择与导频分配，无需训练即可以低导频开销达到接近全CSI的性能。
AI 中文摘要
流体天线系统（FAS）通过从密集候选端口网格中激活少量辐射端口获取空间自由度，但激活哪些端口的决策需要从未测量的端口具备信道知识。现有端口选择研究假设拥有全部或近乎全部信道状态信息（CSI），并将导频开销视为固定成本。我们转而将端口选择与导频分配联合建模为主动推理问题，采用时空生成模型——结合亚波长空间相关性与p阶自回归时间先验——维持所有N个端口信道的高斯置信度，发射机通过最小化预期自由能量（该能量在可达速率、信息增益与端口切换成本间进行权衡），同时选择服务端口及需导频的端口子集。该选择为贪心且子模性的，每时隙复杂度为O(NM)。在含混合前端的441端口网格（n_RF=2K）上，智能体仅测量2.3%的候选端口，即可达到全CSI先知总和速率的91%；对于仍服务的端口，可撤回部分导频：五分之一端口撤回导频仅损失2%总和速率，五分之二端口撤回导频仅损失8%总和速率，且无需训练。
英文摘要
Fluid antenna systems (FAS) obtain spatial degrees of freedom by activating a few radiating ports from a dense grid of candidates, but deciding which ports to activate presupposes channel knowledge at ports that are never measured. Prior port-selection work assumes full or near-full channel state information (CSI) and treats the pilot overhead as a fixed cost. We instead formulate port selection and pilot allocation jointly as active inference. A spatio-temporal generative model -- sub-wavelength spatial correlation together with an order-p autoregressive temporal prior -- maintains a Gaussian belief over all N port channels, and the transmitter chooses both the ports it serves and the subset of them it pilots by minimizing an expected free energy that trades achievable rate against information gain and port-switching cost. Selection is greedy and submodular, at O(NM) per slot. On a 441-port grid with a hybrid front end at n_RF = 2K, the agent attains 91% of a full-CSI genie's sum rate while measuring only 2.3% of the candidate ports; pilots can then be withdrawn from ports that are still served, one fifth of them for 2% of the sum rate and two fifths for 8%. No training is required.
Comments6 pages, 3 figures, 1 table
