AI 中文总结
研究流体天线阵列实时波束成形问题，提出学习型逐块端口激活（L-BPA）方法，通过轻量级卷积网络评分端口，结合多尺度几何排斥和正则化迫零预编码，降低旁瓣电平，提升和速率，相比其他方法有显著优势。
AI 中文摘要
流体天线阵列（FAA）通过激活可重构端口的子集来支持多用户下行链路传输。激活掩码共同决定有效信道和稀疏辐射孔径，这需要在和速率、旁瓣抑制、硬件约束和在线复杂度之间取得平衡。信道驱动选择会使激活端口聚类并增加旁瓣，而面向旁瓣的合成通常与信道无关且可能牺牲和速率。本文提出用于实时旁瓣感知FAA下行链路波束成形的学习型逐块端口激活（L-BPA）。L-BPA在每个孔径块中激活固定数量的端口，支持分组切换硬件并限制端口聚类。一个轻量级卷积网络使用多用户信道特征、端口坐标和用户功率统计对端口进行评分。训练将逐块直通掩码与可微峰值旁瓣电平（PSLL）替代相结合。在推理过程中，将学习到的分数与多尺度几何排斥相结合，然后在简化的有效信道上进行正则化迫零预编码。L-BPA相对于均匀稀疏激活将平均PSLL降低了3.26dB，同时实现了略高的和速率。相对于贪婪选择和基于增益的选择，它还分别将PSLL降低了8.13dB和10.10dB且无需迭代在线搜索。
英文摘要
Fluid antenna arrays (FAAs), support multiuser downlink transmission by activating a subset of reconfigurable ports. The activation mask jointly determines the effective channel and the sparse radiating aperture, which requires a balance among sum rate, sidelobe suppression, hardware constraints, and online complexity. Channel driven selection can cluster active ports and increase sidelobes, whereas sidelobe oriented synthesis is typically channel independent and can sacrifice sum rate. This paper proposes learned blockwise port activation (L-BPA), for real time sidelobe aware FAA downlink beamforming. L-BPA activates a fixed number of ports in each aperture block, which supports grouped switching hardware and limits port clustering. A lightweight convolutional network scores ports using multiuser channel features, port coordinates, and user power statistics. Training combines blockwise straight through masks with a differentiable peak sidelobe level (PSLL), surrogate. During inference, learned scores are combined with multiscale geometric repulsion, followed by regularized zero forcing precoding over the reduced effective channel. L-BPA reduces the average PSLL by 3.26 dB relative to uniform sparse activation while achieving a slightly higher sum rate. It also reduces the PSLL by 8.13 dB and 10.10 dB relative to greedy and gain based selection, respectively, without iterative online search.
