Low Overhead Hybrid-Field Channel Estimation for Hybrid Beamforming XL-MIMO Systems

As a pivotal enabling technology for 6G wireless networks, extra-large scale multiple-input multiple-output (XL-MIMO) systems present unique hybrid-field channel character-istics that necessitate joint consideration of near-field and far-field components. While existing solutions predominantly focus on pure near-field scenarios, the critical challenge in hybrid-field environments lies in effectively decoupling these spatially diverse channel components under practical constraints. This paper proposes a novel low overhead channel estimation frame-work for hybrid-field XL-MIMO systems. Our approach begins by reconstructing the angular domain channel representation through optimized hybrid beamforming architecture. Specifically, we develop a phase shift matrix optimization strategy that enables accurate angular domain recovery under stringent pilot constraints. The proposed spatial filtering technique then achieves effective field separation: far-field components are extracted through joint analysis of beamwidth characteristics and amplitude distributions, while residual near-field components are sub-sequently recovered via compressed sensing-based reconstruction. The proposed algorithm can reconstruct hybrid-field channel estimation with low pilot overhead.