可重构流体天线系统的低复杂度信道估计
Low-Complexity Channel Estimation for Reconfigurable Fluid Antenna System
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中文总结 AI 辅助
针对电磁可重构流体天线系统，本文提出一种利用信道稀疏性的低复杂度信道参数估计框架，通过联合优化数字与电磁预编码器，在低复杂度下实现信道参数的准确恢复。
中文摘要 AI 辅助
电磁可重构流体天线系统（ER-FAS）通过动态控制每个单元的辐射方向图，提供额外的电磁（EM）域自由度，从而提升无线通信的功率效率。然而，这些能力的有效利用关键依赖于准确的信道估计，而现有研究对此关注有限。本文提出一种适用于下行宽带系统的低复杂度信道参数估计框架，利用了信道的稀疏性。该框架采用基于综合的可重构性模型，其中每个天线通过有限组电磁基函数生成所需的波束方向图。基于此模型，本文构建了数字预编码器与电磁预编码器的联合优化，目标是最小化信道参数的克拉美罗下界。仿真结果表明，所提出的混合波束成形与估计方法能够在保持低计算复杂度的同时，实现信道参数的准确恢复。
英文摘要
Electromagnetically reconfigurable fluid antenna system (ER-FAS) provides additional electromagnetic (EM)-domain degrees of freedom by enabling dynamic control of per-element radiation patterns, thereby improving power efficiency in wireless communications. The effective exploitation of these capabilities, however, critically depends on accurate channel estimation, which has received limited attention in prior studies. This paper presents a low-complexity channel parameter estimation framework for downlink wideband systems that leverages available channel sparsity. A synthesis-based reconfigurability model is considered, where each antenna generates desired beampatterns using a finite set of EM basis functions. Based on this model, a joint optimization of digital and EM precoders is formulated with the objective of minimizing the Cramér-Rao lower bound of the channel parameters. Simulation results demonstrate that the proposed hybrid beamforming and estimation approach enables accurate recovery of channel parameters while maintaining low computational complexity.
