arXiv:2610.29534v1 Announce Type: new Abstract: Mobile base stations (MBSs), including aerial and ground-based platforms, provide flexible wireless coverage through adaptive positioning. However, jointly optimizing their trajectories and digital beamforming is challenging because movement changes channel conditions and interference, while beamforming determines the resulting signal and interference powers. These coupled decisions also account for how current movements affect future positions and communication performance under mobility and safety constraints. We formulate this problem as a constrained Markov decision process that maximizes the time-averaged sum signal-to-interference-plus-noise ratio (SINR), subject to mobility, restricted-zone avoidance, and transmit-power constraints. To support long-term planning and reduce the extensive environment interactions required by model-free reinforcement learning, we develop a SafeDreamer-based world model framework that learns latent system dynamics from experience and optimizes a joint trajectory and beamforming policy through imagined trajectories. Feasibility-preserving action mappings and safety cost signals incorporate operational constraints into policy learning. Simulation results demonstrate that the proposed framework improves the time-averaged sum SINR by an average of 7\% and 9\% compared with proximal policy and deep Q-network baselines, respectively, while achieving higher sample efficiency and more reliable constraint satisfaction.