  We put forward a new algorithmic solution to the massive unsourced random
access (URA) problem, by leveraging the rich spatial dimensionality offered by
large-scale antenna arrays. This paper makes an observation that spatial
signature is key to URA in massive connectivity setups. The proposed scheme
relies on a slotted transmission framework but eliminates the need for
concatenated coding that was introduced in the context of the coupled
compressive sensing (CCS) paradigm. Indeed, all existing works on CCS-based URA
rely on an inner/outer tree-based encoder/decoder to stitch the slot-wise
recovered sequences. This paper takes a different path by harnessing the
nature-provided correlations between the slotwise reconstructed channels of
each user in order to put together its decoded sequences. The required
slot-wise channel estimates and decoded sequences are first obtained through
the hybrid generalized approximate message passing (HyGAMP) algorithm which
systematically accommodates the multiantenna-induced group sparsity. Then, a
channel correlation-aware clustering framework based on the
expectation-maximization (EM) concept is used together with the Hungarian
algorithm to find the slotwise optimal assignment matrices by enforcing two
clustering constraints that are very specific to the problem at hand. Stitching
is then accomplished by associating the decoded sequences to their respective
users according to the ensuing assignment matrices. Exhaustive computer
simulations reveal that the proposed scheme can bring performance improvements,
at high spectral efficiencies, as compared to a state-of-the-art technique that
investigates the use of large-scale antenna arrays in the context of massive
URA.
