  This paper investigates the unsourced random access (URA) problem with a
massive multiple-input multiple-output receiver that serves wireless devices in
the near-field of radiation. We employ an uncoupled transmission protocol
without appending redundancies to the slot-wise encoded messages. To exploit
the channel sparsity for block length reduction while facing the collapsed
sparse structure in the angular domain of near-field channels, we propose a
sparse channel sampling method that divides the angle-distance (polar) domain
based on the maximum permissible coherence. Decoding starts with retrieving
active codewords and channels from each slot. We address the issue by
leveraging the structured channel sparsity in the spatial and polar domains and
propose a novel turbo-based recovery algorithm. Furthermore, we investigate an
off-grid compressed sensing method to refine discretely estimated channel
parameters over the continuum that improves the detection performance.
Afterward, without the assistance of redundancies, we recouple the separated
messages according to the similarity of the users' channel information and
propose a modified K-medoids method to handle the constraints and collisions
involved in channel clustering. Simulations reveal that via exploiting the
channel sparsity, the proposed URA scheme achieves high spectral efficiency and
surpasses existing multi-slot-based schemes. Moreover, with more measurements
provided by the overcomplete channel sampling, the near-field-suited scheme
outperforms its counterpart of the far-field.
