Pinching Antennas (PAs) represent a revolutionary flexible antenna technology that leverages dielectric waveguides and electromagnetic coupling to mitigate large-scale path loss. This letter is the first to explore channel estimation for Pinching-Antenna SyStems (PASS), addressing their uniquely ill-conditioned and underdetermined channel characteristics. In particular, two efficient deep learning-based channel estimators are proposed. 1) PAMoE: This estimator incorporates dynamic padding, feature embedding, fusion, and mixture of experts (MoE) modules, which effectively leverage the positional information of PAs and exploit expert diversity. 2) PAformer: This Transformer-style estimator employs the self-attention mechanism to predict channel coefficients in a per-antenna manner, which offers more flexibility to adaptively deal with dynamic numbers of PAs in practical deployment. Numerical results demonstrate that 1) the proposed deep learning-based channel estimators outperform conventional methods and exhibit excellent zero-shot learning capabilities, and 2) PAMoE delivers higher channel estimation accuracy via MoE specialization, while PAformer natively handles an arbitrary number of PAs, trading self-attention complexity for superior scalability.

翻译：捏合天线是一种革命性的柔性天线技术，它利用介质波导和电磁耦合来缓解大规模路径损耗。本文首次探讨了捏合天线系统的信道估计问题，针对其特有的病态欠定信道特性提出了解决方案。具体而言，我们提出了两种基于深度学习的高效信道估计器：1) PAMoE：该估计器融合了动态填充、特征嵌入、特征融合与专家混合模块，能有效利用捏合天线的位置信息并发挥专家多样性优势；2) PAformer：这种Transformer架构的估计器采用自注意力机制以逐天线方式预测信道系数，在实际部署中能灵活适应动态变化的捏合天线数量。数值结果表明：1) 所提出的深度学习信道估计器性能优于传统方法，并展现出卓越的零样本学习能力；2) PAMoE通过专家混合专业化实现了更高的信道估计精度，而PAformer则天然支持任意数量的捏合天线，以自注意力计算复杂度为代价获得了优异的可扩展性。