In this paper, we design a novel two-phase unsourced random access (URA) scheme in massive multiple input multiple output (MIMO). In the first phase, we collect a sequence of information bits to jointly acquire the user channel state information (CSI) and the associated information bits. In the second phase, the residual information bits of all the users are partitioned into sub-blocks with a very short length to exhibit a higher spectral efficiency and a lower computational complexity than the existing transmission schemes in massive MIMO URA. By using the acquired CSI in the first phase, the sub-block recovery in the second phase is cast as a compressed sensing (CS) problem. From the perspective of the statistical physics, we provide a theoretical framework for our proposed URA scheme to analyze the induced problem based on the replica method. The analytical results show that the performance metrics of our URA scheme can be linked to the system parameters by a single-valued free entropy function. An AMP-based recovery algorithm is designed to achieve the performance indicated by the proposed theoretical framework. Simulations verify that our scheme outperforms the most recent counterparts.

翻译：本文设计了一种新型双阶段无源随机接入（URA）方案，应用于大规模多输入多输出（MIMO）系统。第一阶段，我们收集一串信息比特，以联合获取用户信道状态信息（CSI）及关联的信息比特。第二阶段，将所有用户的剩余信息比特分割成极短长度的子块，与现有大规模MIMO URA传输方案相比，该方案展现出更高的频谱效率和更低的计算复杂度。利用第一阶段获取的CSI，第二阶段中的子块恢复被建模为压缩感知（CS）问题。从统计物理学视角出发，我们为所提出的URA方案构建了一个理论框架，基于复制方法对由此引发的问题进行分析。分析结果表明，URA方案的性能指标可通过单值自由熵函数与系统参数建立关联。我们设计了一种基于近似消息传递（AMP）的恢复算法，以实现该理论框架所预示的性能。仿真验证表明，我们的方案优于最新同类方案。