arXiv:2610.36880v1 Announce Type: new Abstract: The achievable gains of multiple-input multiple-output (MIMO) systems depend on channel state information (CSI), whose acquisition consumes pilot and feedback resources for every user in every coherence interval. Meanwhile, the propagation environment that shapes the channel persists. Exploiting such information will help to greatly reduce the overhead of CSI acquisition. This paper investigates the spatial MIMO channel rendering problem, which learns a latent beamspace channel scene from sparse observations and renders the long-term transmit and receive beam pair power map at unobserved user locations. Motivated by 3D Gaussian splatting in visual scene rendering, the proposed MIMO Gaussian splatting (MIMO-GS) represents the scene with pairs of receive and transmit 3D Gaussians, projects them from the user and base station onto the corresponding beam domains, and accumulates the beam associations through beam pair splatting. On a simulated urban scenario and measured indoor channels, MIMO-GS renders 5.5 times faster than ray tracing without a calibrated replica. Applied to multiuser beam management, the rendered maps allow users and transmit beams to be scheduled without sounding while accounting for the inter-user interference, and the net sum rate reaches 98% of the genie-aided benchmark, 19% above a position-based predictor.