  A joint sparse-regression-code (SPARC) and low-density-parity-check (LDPC)
coding scheme for multiple-input multiple-output (MIMO) massive unsourced
random access (URA) is proposed in this paper. Different from the
state-of-the-art covariance-based maximum likelihood (CB-ML) detection scheme,
we first split users' messages into two parts. The former part is encoded by
SPARCs and tasked to recover part of the messages, the corresponding channel
coefficients as well as the interleaving patterns by compressed sensing. The
latter part is coded by LDPC codes and then interleaved by the
interleave-division multiple access (IDMA) scheme. The decoding of the latter
part is based on belief propagation (BP) joint with successive interference
cancellation (SIC). Numerical results show our scheme outperforms the CB-ML
scheme when the number of antennas at the base station is smaller than that of
active users. The complexity of our scheme is with the order
$\mathcal{O}\left(2^{B_p}ML+\widehat{K}ML\right)$ and lower than the CB-ML
scheme. Moreover, our scheme has higher spectral efficiency (nearly $15$ times
larger) than CB-ML as we only split messages into two parts.
