Adaptive Joint Sparse Bayesian Approaches for Near-Field Channel Estimation

Abstract

The deployment of extremely large-scale MIMO (XL-MIMO) and short-wavelength signaling enhances communication capabilities and improves spectrum efficiency for future sixth-generation (6G) wireless communication. However, users may potentially be located in the near-field region due to the sharp increase in antenna array aperture. In the near-field region, the signal wave is spherical wave. Thus, the consideration of spatial angle and distance requires the development of novel channel estimation algorithms to reduce codebook overhead. This paper develops a novel scheme based on a low-size adaptive codebook to reconstruct the near-field channel. Initially, it is investigated that the angle spread for one channel path component is confined to a certain angular spatial region, which demonstrates the sparsity inherent in angular domain. Exploiting the angular sparsity inherent, we propose a novel adaptive joint sparse Bayesian learning (JSBL) estimation algorithm on all subcarriers to cater to reduce the codebook size. The proposed algorithm captures all spatial angular sparse information and then refines distance information so that the measurement codebook size only depends on the spatial angular resolution. Further, the proposed adaptive JSBL approach is extended to estimate the time-varying near-field channel. Moreover, Bayesian Cramér-Rao Bounds (BCRBs) are derived for quasi-static and temporal scenarios. Numerical simulations are presented to demonstrate that our approaches with low codebook overhead outperform other algorithms based on the angular-domain and polar-domain codebooks.