Subarray Measurement Optimization Algorithm for Hybrid-Field Channel Estimation in UM-MIMO Systems

As a promising technology for 6G networks, ultra-massive multiple-input multiple-output (UM-MIMO) faces substantial problems such as wavefront mismatch, high computational complexity, and reliance on unrealistic assumptions in hybrid field channel estimation. To address these issues, we propose a novel subarray-based measurement optimization framework. Initially, we adopt a wavefront decomposition model based on subarray. By decomposing the spherical wavefront into localized planar wavefront approximations, this model effectively resolves the wavefront mismatch and eliminates the dependence on the ratio factor while still ensuring high accuracy. Subsequently, a noise-robust measurement optimization algorithm is proposed to achieve a dual reduction in complexity. Specifically, a dynamic signal-to-noise ratio (SNR)-aware measurement optimization approach, utilizing heap sorting, prioritizes high-confidence measurements. Meanwhile, the two-dimensional inverse fast Fourier transform (2D-IFFT) estimation method reduces the unit computational load through parallel subarray processing. Simulation results demonstrate that our proposed algorithm consistently outperforms existing approaches across diverse SNR levels and ratio factors, with significant advantages in low-SNR scenarios. Additionally, in 2D array deployments, it shows an improvement of 3 dB in channel estimation error performance, highlighting its robustness and scalability in both configurations.