Movable antenna (MA) has emerged as a promising technology for future wireless systems. Compared with traditional fixed-position antennas, MA improves system performance by antenna movement to optimize channel conditions. For multiuser wideband MA systems, this paper proposes deep learning-based framework integrating channel estimation (CE), antenna position optimization, and beamforming, with a clear workflow and enhanced efficiency. Specifically, to obtain accurate channel state information (CSI), we design a two-stage CE mechanism: first reconstructing the channel matrix from limited measurements via compressive sensing, then introducing a Swin-Transformer-based denoising network to refine CE accuracy for subsequent optimization. Building on this, we address the joint optimization challenge by proposing a Transformer-based network that intelligently maps CSI sequences of candidate positions to optimal MA positions while combining a model-driven weighted minimum mean square error (WMMSE) beamforming approach to achieve better performance. Simulation results demonstrate that the proposed methods achieve superior performance compared with existing counterparts under various conditions. The codes about this work are available at https://github.com/ZiweiWan/Code-4-DL-MA-CE-BF.

翻译：可移动天线（MA）已成为未来无线通信系统中一项前景广阔的技术。与传统固定位置天线相比，MA通过天线移动来优化信道条件，从而提升系统性能。针对多用户宽带MA系统，本文提出了一种基于深度学习的框架，该框架集成了信道估计（CE）、天线位置优化与波束成形，具有清晰的工作流程和更高的效率。具体而言，为获取准确的信道状态信息（CSI），我们设计了一种两阶段CE机制：首先通过压缩感知从有限测量中重建信道矩阵，随后引入一个基于Swin-Transformer的去噪网络以提升CE精度，供后续优化使用。在此基础上，我们通过提出一个基于Transformer的网络来解决联合优化难题，该网络能够智能地将候选位置的CSI序列映射至最优MA位置，同时结合模型驱动的加权最小均方误差（WMMSE）波束成形方法以获得更佳性能。仿真结果表明，在各种条件下，所提方法相比现有方案均能实现更优的性能。本工作的相关代码可在 https://github.com/ZiweiWan/Code-4-DL-MA-CE-BF 获取。