Dynamic Hybrid-field Channel Estimation for Extremely Large-scale Massive MIMO

• Published in: 2024 IEEE Wireless Communications and Networking Conference (WCNC) pp. 1 - 6
• Main Authors: Yan, Xingyun, Yuan, Jide
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.04.2024
• Subjects: Apertures; Channel estimation; channel estimation (CE); Dynmic hybrid-field channel; Estimation; extremely large-scale massive MIMO (XL-MIMO); Heuristic algorithms; Massive MIMO; Matching pursuit algorithms; Simulation
• ISSN: 1558-2612
• DOI: 10.1109/WCNC57260.2024.10570520

The significantly increased array aperture and the higher communication frequency band in extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems con-siderably expand the near-field propagation region compared to conventional MIMO. Hybrid-field channels, encompassing both near- and far-fields, have become more common in propagation environments. Current channel estimation (CE) methods rely on angular- and polar-domain sparsity but require a prior knowledge of near- and far-field characteristics, which can be impractical for mobile users. Initially, we introduce a beamwidth-based determination criterion for distinguishing near- and far-field path components by analyzing the angular-domain power spectrum of the far-field signals. Then, we introduce the dynamic orthogonal matching pursuit (DOMP) algorithm to reconstruct individual near- and far-field channel paths, thereby recovering the hybrid-field channel. Our simulation results illustrate that our approach can achieve superior CE performance even without relying on a priori information regarding near- and far-field path components.