  Accurate channel estimation is critical to fully exploit the beamforming
gains when communicating with extremely large aperture arrays. The propagation
distances between the user and receiver, which potentially has thousands of
antennas/elements, are such that they are located in the radiative near-field
region of each other when considering the Fraunhofer distance of the entire
array. Therefore, it is imperative to consider near-field effects to achieve
proper channel estimation. This paper proposes a parametric multi-user
near-field channel estimation algorithm based on MUltiple SIgnal Classification
(MUSIC) method to obtain the essential parameters describing the users'
locations. We derive the estimated channel by incorporating the estimated
parameters into the near-field channel model. Additionally, we implement a
least-squares-based estimation corrector, resulting in a precise near-field
channel estimation. Simulation results demonstrate that our proposed scheme
outperforms classical least-squares and minimum mean-square error channel
estimation methods in terms of normalized beamforming gain and normalized
mean-square error.
