  Extremely large-scale multiple-input-multiple-output (XL-MIMO) at
millimeter-wave (mmWave) and terahertz (THz) bands plays an important role in
supporting extreme high beamforming gain as well as ultra-wideband spectrum
resources. Unfortunately, accurate wideband XL-MIMO channel estimation suffers
from the new challenge called as the near-field beam split effect. Prior works
either neglect the accurate near-field channel model or fail to exploit the
beam split effect, resulting in poor channel estimation accuracy for wideband
XL-MIMO. To tackle this problem, this paper proposes a bilinear pattern
detection (BPD) based approach to accurately recover the wideband XL-MIMO
channel. Specifically, by analyzing the characteristics of near-field wideband
channels, we first reveal the bilinear pattern of the near-field beam split
effect, which implies that the sparse support set of near-field channels in
both the angle and the distance domains can be regarded as a linear function
against frequency. Then, inspired by the classical simultaneously orthogonal
matching pursuit technique, we use the bilinear pattern to estimate the
angle-of-arrival (AoA) and distance parameters of each near-field path
component at all frequencies. In this way, the entire wideband XL-MIMO channel
can be recovered by compressed sensing algorithms. Moreover, we provide the
computational complexity of the proposed algorithm compared with existing
algorithms. Finally, simulation results demonstrate that our scheme can achieve
the accurate estimation of the near-field wideband XL-MIMO channel in the
presence of near-field beam split effect.
