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.

翻译：本文建议对多投入多输出(MIMO)大规模无源随机访问(URA)采用联合稀释递增代码(SPAC)和低密度平衡检查(LDPC)编码办法。 后一部分的编码办法不同于基于最先进的共变最大可能性( CB-ML) 的检测办法, 我们首先将用户的信息分成两部分。 前一部分由SPARC 编码, 任务是通过压缩感应来回收部分信息、 相应的频道系数 以及中间模式。 后一部分由LDPC 代码编码, 后一部分由跨离心多输出( URA) 方案( URA ) 。 后一部分的解码办法基于信仰传播( BBP) 和连续的干扰取消( SIC ) 。 数字结果显示, 当基站的天线数量小于活跃用户时, 我们的计划的复杂性在于以美元/\\\\\\\\\\\\\\\\\\\\\ mLMLF_MLMLLL( ) 更低的系统系统。 。 我们的系统只有以美元/\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\