Near-Field Channel Estimation for Ultra-Massive MIMO Antenna Array with Hybrid Architecture

Ultra massive multiple-input-multiple-output (UM-MIMO) has emerged as a prospective capacity-enhancing technology for next generation (NG) networks. To harness its full potential, accurate channel estimation with low pilot overhead becomes paramount. The combination of large antenna arrays and high frequency bands transitions the wireless communication from the far-field to the near-field realm. This transition presents a unique challenge, as channel sparsity in the angular domain becomes unattainable in the presence of near-field spherical wavefronts. Nonetheless, polar-domain sparsity is achievable, allowing existing near-field channel estimation methods to use polar codebooks over the classical angle-based codebooks. However, a major challenge in utilizing polar domain sparsity is the size of the polar codebook, demanding sampling of both distance and angle points across the entire near-field. In this work, we investigate near-field channel response in the beam space domain to redefine the boundaries of polar domain sparsity. We propose a novel polar codebook leveraging our results of sparsity analysis and beamfocusing property of near-field. Unlike existing work, the proposed polar codebook design is agnostic to user range information and has considerably reduced dimensions. Exploiting the new polar codebook, we present the beam focused simultaneous orthogonal matching pursuit (BF-SOMP) algorithm for efficient near-field channel estimation. Simulation results demonstrate that the proposed algorithm surpasses contemporary methods in terms of normalized mean square error (NMSE) and reduced computational complexity.