arXiv:2611.05989v1 Announce Type: new Abstract: Six-dimensional movable antenna (6DMA) technology allows a base station (BS) to adjust the three-dimensional (3D) positions and 3D rotations, i.e., the 6D poses, of its surfaces. Many existing 6DMA designs select the poses based on prior channel knowledge. However, the channels at a new pose can be estimated only after the surfaces have moved to this pose, and the limited movement speeds of the surfaces restrict the poses reachable at the next time step. To address these challenges, we propose a learning-based multimodal sensing-aided trajectory and beamforming design for multiuser 6DMA systems with mobile users and surrounding objects. The BS observes the users and surrounding objects with RGB images, LiDAR point clouds, and GPS positions, independently of the poses of the surfaces. Since a surface may need several time steps to reach a favorable pose, the BS designs a 6DMA trajectory over several future time steps and executes only its first step, following receding-horizon control. We propose a neural network, in which user-centric cross-modal attention and temporal self-attention modules relate each user to the scene and its sensing history, and surface-to-user attention relates each surface to the users. A projection layer keeps the poses feasible under the geometric and kinematic constraints, and a graph attention module allocates the transmit power along the pilot-based beamforming directions. Performance evaluations using the CARLA driving simulator and Sionna ray tracing (RT) demonstrate significant minimum-rate gains of the proposed design over fixed-position antennas. Ablation studies validate the contributions of multimodal sensing and the proposed attention modules to these gains. The results further highlight the importance of designing the trajectory several time steps ahead, which is more beneficial under tighter speed limits of the surfaces.