论文标题
SPARC-LDPC编码MIMO大量未包含的随机访问
SPARC-LDPC Coding for MIMO Massive Unsourced Random Access
论文作者
论文摘要
本文提出了针对多输入多输出（MIMO）大规模无源随机访问（URA）的关节稀疏回归代码（SPARC）和低密度 - 偏见检查（LDPC）编码方案。与最先进的协方差最大似然（CB-ML）检测方案不同，我们首先将用户的消息分为两个部分。前一部分是由SPARCS编码的，并任务是恢复部分消息，相应的通道系数以及通过压缩感应进行交织的模式。后一部分由LDPC代码编码，然后通过InterLeave-Division多访问（IDMA）方案进行交织。后一部分的解码是基于信仰传播（BP）的连续干扰取消（SIC）。数值结果表明，当基站的天线数小于活跃用户时，我们的方案优于CB-ML方案。我们方案的复杂性是$ \ MATHCAL {O} \ left（2^{b_p} ml+\ wideHat {k} ml \ right）$，低于CB-ML方案。此外，由于我们仅将消息分为两个部分，因此我们的计划比CB-ML具有更高的光谱效率（近15美元$ $倍）。
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.
