In this paper, we introduce movable antenna (MA) technology into orthogonal time frequency space (OTFS) systems to enable wavelength-level antenna position optimization under imperfect channel state information (CSI), thereby mitigating deep fading. To accurately acquire CSI, we develop a sparse Bayesian learning method with variational inference (SBLVI) method. Based on estimated CSI, we formulate an MA position optimization problem with the objective of maximizing channel gain. Due to the highly non-convex character of the problem, we further develop a deep reinforcement learning (DRL) strategy to intelligently optimize MA positions. Simulation results show that the proposed SBLVI method significantly improves channel estimation accuracy over benchmark methods, and MA position optimization based on estimated CSI achieves substantially higher channel gains than the fixed-position antenna (FPA), demonstrating the effectiveness of the proposed MA-assisted OTFS system.

翻译：本文提出将移动天线（MA）技术引入正交时频空间（OTFS）系统，以在非理想信道状态信息（CSI）条件下实现波长级天线位置优化，从而缓解深衰落。为精确获取CSI，我们开发了一种结合变分推理的稀疏贝叶斯学习方法（SBLVI）。基于估计得到的CSI，我们建立以信道增益最大化为目标的MA位置优化问题。鉴于该问题的高度非凸特性，我们进一步提出深度强化学习（DRL）策略以智能优化MA位置。仿真结果表明，与基准方法相比，所提出的SBLVI方法显著提高了信道估计精度；基于估计CSI的MA位置优化相比固定位置天线（FPA）实现了更高的信道增益，验证了所提出的MA辅助OTFS系统的有效性。