Site-Specific Learning for Low-Overhead Multi-User MIMO Beamforming

Cheng-Jie Zhao, Zhaolin Wang, Zongyao Zhao, Yuanwei Liu

A low-overhead site-specific multi-user multiple-input multiple-output (MU-MIMO) beamforming framework is proposed. Conventional limited-feedback MU-MIMO relies on channel state information reference signal (CSI-RS) transmission and user feedback before grouping and beamforming, which requires substantial online overhead when the antenna dimension and candidate-user pool are large. To reduce this burden, the proposed framework exploits site-specific information (SSI), which captures local radio propagation features. By learning the mapping from low-overhead beam-domain observations to effective transmit spatial subspaces of users, the BS can infer inter-user separability before high-resolution CSI acquisition and construct a compact group-level CSI acquisition subspace for the selected users. This site-specific design can be implemented within the standard limited-feedback procedure using synchronization signal block (SSB)-based reference signal received power (RSRP) fingerprints for subspace inference and CSI-RS feedback for low-dimensional CSI refinement. Extensive numerical results demonstrate that the proposed framework can identify compatible user groups before CSI-RS acquisition, preserve most scheduled-user channel energy in a compact group subspace, and achieve higher effective rates than conventional systems with significantly lower overhead and user-side processing burden.